Structural neuroimaging in sport-related concussion

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Structural neuroimaging in sport-related concussion Erin D. Bigler⁎ Department of Psychology, Brigham Young University, Provo, UT, USA Neuroscience Center, Brigham Young University, Provo, UT, USA

A R T I C L E I N F O

A B S T R A C T

Keywords: Computed tomography (CT) Magnetic resonance imaging (MRI) Sports-related concussion (SRC) Mild traumatic brain injury (mTBI) Quantitative neuroimaging Diffusion tensor imaging

Structural neuroimaging of athletes who have sustained a sports-related concussion (SRC) can be viewed as either standard clinical imaging or with advanced neuroimaging methods that quantitatively assess brain structure. Negative findings from conventional computed tomography (CT) or magnetic resonance imaging (MRI) are the norm in SRC. Nonetheless, these conventional measures remain the first line of neuroimaging of the athlete as they do detect clinically significant pathologies, when present, such as hemorrhagic abnormalities in the form of hematomas, contusions and mircobleeds along with regions of focal encephalomalacia or other signal abnormalities, with CT best capable of detecting skull fractures. However, advanced neuroimaging techniques hold particular promise in detecting subtle neuropathology in the athlete which standard clinical neuroimaging cannot. To best understand what conventional as well as quantitative neuroimaging methods are detecting in SRC, this review begins by covering basic neuroanatomical principles associated with mild traumatic brain injury (mTBI) and the brain regions most vulnerable to injury from SRC, as these regions define where advanced neuroimaging methods most likely detect abnormalities. Advanced MRI techniques incorporate quantitative metrics that include volume, shape, thickness along with diffusion parameters that provide a more fine-grained analysis of brain structure. With advancements in image analysis, multiple quantitative neuroimaging metrics now can be utilized in assessing SRC. Such multimodality approaches are particularly relevant and important for assessing white matter and network integrity of the brain following injury, including SRC. This review focuses just on the structural side of neuroimaging in SRC, but these techniques also are being integrated with functional neuroimaging, where the combination of the two approaches may provide superior methods in assessing the pathological effects of SRC.

Standard clinical neuroimaging with computed tomography (CT) and magnetic resonance imaging (MRI) typically do not reveal abnormalities in sports-related concussion (SRC; see Bigler and Orrison, 2004).1 In contrast, advanced magnetic resonance (MR)-based neuroimaging techniques have the potential to detect subtle neuropathological changes associated with SRC. The importance of detecting subtle pathology in SRC is critical to not only understanding the nature of the injury but potentially, will have widespread clinical import for managing SRC, return to play decision making and tracking an athlete's neurological and neuropsychiatric status over a lifetime. Current clinical decision making informed only with negative conventional neuroimaging and existing standards that rely on traditional approaches to neurocognitive and neurobehavioral assessments have fallen short of addressing the nature and degree of potential brain pathology associated with SRC. As recently shown by Tator et al. (2016), within a

University-based concussion clinic, the median postconcussion syndrome (PCS) symptom duration in that clinical sample was seven months. The majority of the 221 individuals that participated in the Tator et al. investigation had sustained SRC or concussion associated with some recreational activity with almost 12% of this sample reporting PCS lasting beyond two years (see also Hiploylee et al., 2017). Of particular importance for the current review, the Tator et al. (2016) study excluded all patients with positive neuroimaging findings on conventional CT or MRI. As such traditional neuroimaging generally do not detect nor illuminate the role that subtle brain pathology may play in SRC, while advanced quantitative methods have that potential (Mitra et al., 2016; Sussman et al., 2017). In the absence of any conventional neuroimaging finding, how does one understand the symptoms, problems and complaints of the athlete with SRC? Since the symptoms associated with PCS – headache, fatigue,

⁎

Corresponding author at: Department of Psychology and, Neuroscience Center, 1001 SWKT, Brigham Young University, Provo, UT 84602, USA E-mail address: [email protected]. In this review the term ‘head injury’ merely refers to the athlete having sustained some kind of blow, impact and/or acceleration/deceleration to the head without necessarily implying brain injury. The term concussion is used interchangeably with mild traumatic brain injury (mTBI). Sports-related brain injury means that the athlete met clinical criteria for having sustained a TBI. Sports-related head injury in this review merely refers to some impact involving the head during a sports activity. 1

http://dx.doi.org/10.1016/j.ijpsycho.2017.09.006 Received 11 January 2017; Received in revised form 3 September 2017; Accepted 7 September 2017 0167-8760/ © 2017 Elsevier B.V. All rights reserved.

Please cite this article as: Bigler, E.D., International Journal of Psychophysiology (2017), http://dx.doi.org/10.1016/j.ijpsycho.2017.09.006

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1. The meaning of “subtle” pathology in concussion

visual disturbances, vertigo, sleep disruption, mood dysregulation, inefficient memory, problems with concentration, etc. – are all nondescript and nonspecific (Meyer and Arnett, 2015), such symptoms provide no ability to differentiate possible underlying brain pathology specific to SRC or that which may have predated the injury, as in the Tator et al. (2016) study 26.2% of the participants that met inclusion criteria had a diagnosable pre-injury neuropsychiatric disorder prior to sustaining a concussion. For some is PCS following SRC merely an extension of an existing disorder? Other than the fact that PCS symptom reporting occurred after some trauma involving the head, what biomarker is there that provides the clinician or researcher with any objective information about the brain injury or even that the brain was injured? Likewise, traditional neuropsychological approaches have no ability to detect actual brain pathology, only inferences about neural systems that may be affected (see Bigler, 2016b). As pointed out by Kontos et al. (2016) and Prince and Bruhns (2017), performance on the neuropsychological examination in someone following a concussion potentially will be influenced by a host of factors not necessarily related to a specific brain abnormality, since pain, mood dysregulation, poor sleep hygiene and a variety of other somatic factors may affect the individual who sustained a concussive brain injury (see also Silver, 2012). On the other hand, advanced neuroimaging analyses have that potential to specifically identify SRC-related neuropathology, which is the focus of this review. It needs be emphasized that most advanced neuroimaging methods have only been developed within the last decade, so none are ready for clinical implementation in the management of SRC as of this writing (see Broglio et al., 2017), conclusions that have been reinforced by other reviews (Dimou and Lagopoulos, 2014), meta-analyses (Tarnutzer et al., 2016) and consensus conferences on sport (Kamins et al., 2017; McCrea et al., 2017). The foundation for this review is that understanding SRC depends, in part, on an informative understanding about relevant brain anatomy in conjunction with common features associated with acceleration/ deceleration deformation of the brain within the cranial vault following impact. While each injury is unique to that individual, there are some shared injury dynamics and regions of interest (ROI) that are most common to SRC and brain injury. This will constitute the first part of this review. Next, this review will examine the structural neuroimaging findings, albeit infrequent, that may be visibly identifiable in conventional CT and/or MRI reflective of sports-related brain injuries, where CT is typically only performed in an acute setting as part of emergent medical decision making or to evaluate for suspect facial bone or skull fractures. Otherwise, MRI is the method of choice because of its superiority in detecting structural brain pathology associated with traumatic brain injury (TBI), including those from SRC (Ellis et al., 2016). Some examples of acute CT findings will be discussed, but the main focus will involve MR techniques. The standard clinical approach to interpreting a MRI scan involves the identification of a visible abnormality and how brain anatomy conforms to expected age and typical developed brains from healthy individuals, with no history of brain injury and/or neurological or neuropsychiatric disorder. However, not detected by just “looking” at the scan image, there are now a variety of advanced image analysis techniques that use various MR metrics to quantify the size, shape, thickness, volume, or diffusion properties of a given ROI that may reflect structural pathology associated with TBI. This review will not address functional neuroimaging findings in SRC, including MR spectroscopy, as this has recently been reviewed elsewhere (see Kamins et al., 2017; McCrea et al., 2017). Although referred to at times in this review, there will be no in depth coverage of the neuroimaging literature on chronic traumatic encephalopathy (Gangolli et al., 2017; Raji et al., 2016; Shetty et al., 2016) or recent post-mortem analyses of CTE findings in professional (Mez et al., 2017) or collegiate athletes (Mez et al., 2016) and the potential to image such abnormalities.

This review is focused on the structural side of brain imaging in SRC, where the neuroanatomical appearance and findings of brain structure are central in the identification of healthy brain parenchyma. However, in the living individual, the best that currently can be achieved is a macroscopic view of brain parenchyma. While investigators who use diffusion MR techniques, like diffusion tensor imaging (DTI) like to imply “microstructural” pathology, but with the macroscopic scale of contemporary neuroimaging being limited to acquisition parameters that typically assess tissue at the millimeter level, means that anything more molecular, must be inferred. Nonetheless, what does it mean if there is even a 1 mm × 1 mm × 1 mm voxel difference observed? First, neuroimaging analyses examining the entire brain typically would not consider a single, isolated 1 mm3 voxel as a significant finding, if it were the only finding in all of the analyses. Commonly, a priori established neuroimaging cluster thresholds to be significant involve multiple voxels in a neuroimaging investigation prior to the actual study being conducted (Chumbley et al., 2010). Nonetheless, it is still instructive to consider what is present in a cubic millimeter of tissue. Based on the computational modelling of the mouse brain by Braitenberg (2001), Mills and Tamnes (2014) estimate that one cubic millimeter of gray matter is proportionally comprised of the following: 30% axons, 30% dendrites, 12% dendritic spines, 14% cell bodies and blood vessels, 9% glial cells and 5% extracellular space. Translating this into something more relevant in understanding concussion, Insel and Landis (2013) estimate that within a single cubic millimeter of cortical gray matter there are approximately 80,000 neurons and 4.5 million synapses. As demonstrated in cases of focal epilepsy, lesions that constitute no more than few millimeters of abnormal tissue, strategically placed, may be the source of widespread network disruption (Jackson et al., 2017). Additionally, modelling of small foci of abnormal electrophysiological activity can be very disruptive to otherwise healthy brain networks (Izhikevich and Edelman, 2008; Omidvarnia et al., 2017). Furthermore, as reflected in the estimates by Mills and Tamnes any MR-defined abnormality detected at the macroscopic level is likely to influence multiple cellular and vascular components, so it is not just affecting neurons. In TBI, white matter is also differentially injured due to its elasticity and deformation characteristics that diverge from gray matter as well as the fact that some white matter tracts have many crossing fibers or bend at different points in their trajectory from origin to terminus (Schmidt et al., 2016; Wright et al., 2017). So where the lesion/abnormality is defined, regardless of its size, may have particular important for sequelae that may emerge. What this means in SRC is that the angle or angles in which the head is struck, or the direction of acceleration/deceleration injury creates unique shear-strain dynamics for each concussion and no uniform area that is consistently injured in every case. 2. Brain anatomy relevant to brain injury Understanding the neuroanatomical outcomes that may accompany sport-related brain injuries, begins with understanding that TBI occurs as a result of brain deformation. As stated in the consensus definition from the ‘International and Interagency Initiative toward Common Data Elements for Research on Traumatic Brain Injury and Psychological Health’ TBI is defined as follows: TBI is defined as an alteration in brain function, or other evidence of brain pathology, caused by an external force (Menon et al., 2010, p. 1637). That the causal event inducing a TBI occurs as a result of an “external force” means that a threshold has to be surpassed to induce sufficient parenchymal deformation to induce brain injury, where tissue deforms beyond its tolerance to maintain physiological and structural integrity. 2

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not the case, coma duration and/or altered mental status would exceed the limits of a mild injury. So given the above mentioned structures, the potential for advanced neuroimaging to detect abnormalities is more likely to be in non-brainstem areas. Although what is presented in Fig. 1 is specific to one investigation (see Hernandez et al., 2015), as already mentioned these regions have been routinely documented in other finite modelling studies of SRC (Darling et al., 2016; Giordano and Kleiven, 2014; Viano et al., 2005), especially the involvement of the corpus callosum (Khong et al., 2016), where disrupted callosal integrity in brain injury has the potential for widespread disruption of neural function, with adverse influences on the speed of neural transmission (Cui et al., 2016). There are several anatomical issues important for understanding SRC and why certain clinical neuroimaging findings may be observed. As a soft, hydrated organ, brain parenchyma is almost 80% water and while there are both elastic and viscous properties they differ across white and gray matter structures (Budday et al., 2015). In FE modelling the linear viscoelastic properties typically characterize white matter as 25% or more stiffer than gray. The falx cerebri and tentorium cerebelli are typically modeled as elastic but distinctly rigid. How an implosive force or impact is directed to the head governs where the greatest deformations occur. As shown by Darling et al. (2016), using a FE method to examine football helmet impact, the greatest strains develop and timing of their occurrence in the brain relates specifically to the orientation of impact. With impact to the crown of the helmet in their modelling scheme, it took 3.75 ms for the corpus callosum to be displaced ≥0.25 mm. The greatest deformations occurred with frontal oblique impact with less from crown impact. As such a complex, interactive dynamic occurs following impact and/or rapid, forceful acceleration/deceleration between the movement of gray and white matter structures and brain surface movements that impact skull compartments. Furthermore, how the brain is held and responds to external force, constrained by its stiffer white matter connectivity and ensconced by meninges and blood vessels defines where likely injuries occur. Ganpule et al. (2017) also used football SRC to model time in the evolution of parenchymal deformation following head impact, with a focus on the toroid distribution to the cortical gray-white matter distribution. Strain gradients at this gray-white matter interface following SRC are dynamic with initial peak brain deformation occurring within the first 10–15 ms but continuing to evolve out to ~27 ms. These studies clearly demonstrated the speed with which the initial injury begins and the widespread deformations in brain parenchyma that follow SRC. Injury severity also plays into this outcome. While there may be loss of consciousness (LOC) with mTBI, by definition any LOC has to be short-lived when the injury is mild. Most definitional frameworks of mTBI require LOC to be < 30 min (Ruff and Jurica, 1999) and in most sports-related injuries, LOC when present at all is typically very shortlived. Fig. 2 is from Ropper and Gorson (2007) which depicts, in alignment with what has just been discussed above using FE modelling of concussion, a classic schematic of where some of the greatest brain parenchymal movement occurs in head injury. Note the position of the upper brainstem as a particularly vulnerable region for movement. Within this midbrain region there are a host of ascending and descending sensory and motor tracts that interface cerebellar and brainstem nuclei with other brain regions, especially those critical for balance, coordination and eye movement. Also housed within the brainstem is the ascending reticular activating system, which also interfaces with the diffuse thalamic projection system (Moruzzi and Magoun, 1949), although other midbrain regions that project to the basal forebrain may also be critical to neural networks that relate to arousal, alertness, consciousness and wakefulness (Fuller et al., 2011; Schiff, 2010). The assumption is that perturbation of these regions in TBI leads to acute and transient alterations in level of consciousness, motor impairments such as acute dystaxia and ocular indicators such as nystagmus and impaired saccadic movement (Samadani et al., 2015).

How best to understand the influence of external force dynamics on brain parenchyma in sports comes from kinematic studies of head injury combined with modelling of brain movement via finite element (FE) methods (Bandak et al., 2015; Stemper and Pintar, 2014; von Holst and Li, 2013). Understanding brain anatomy in the context of FE modelling of brain injuries facilitates understanding where neuropathological changes, either observable or via quantitative image analysis methods will most likely occur. In these modelling studies it is important to recognize that stiffness or viscoelastic properties of tissue is directionally dependent, especially important at certain locations of large tracts, how they are bundled and cross or intertwine and how they interface with cortical and subcortical regions of gray matter (Giordano et al., 2014; Giordano and Kleiven, 2014). All of these physical characteristics of tissue influence measures of water diffusion (anisotropy), the basis for DTI metrics. Accordingly, the first part of this section will outline aspects of basic neuroanatomical principles needed to understand where observable abnormalities occur in SRC, as well as why advanced neuroimaging analysis techniques are so important. Furthermore, understanding the neuroanatomical and neuropathological bases to SRC helps explain the variable ways in which the brain can be injured and where those injuries are most likely to occur. Much of the FE work in SRC has been done in athletes where video of their injuries may be available or via motion sensors or accelerometers embedded in the athlete's equipment, with injury dynamics recreated in the laboratory (Brennan et al., 2016; Broglio et al., 2012; Dorminy et al., 2015; Post et al., 2015). FE techniques can be applied to MRI where three-dimensional (3-D), biomechanical modelling defines where the greatest strain deformation occurs in the brain (Bayly et al., 2012). Such MRI brain modelling can be performed with group as well as individual scan data. Fig. 1, from Hernandez et al. (2015) shows the principle peak deformation in the brain from athletes (American football, boxers and mixed martial arts) fitted with instrumented mouthguards to measure human head impact and motion who sustained a mild TBI (mTBI) associated with loss of consciousness (LOC). There are clear and distinct regions where maximal strain occurred in this modelling, in particular within the corpus callosum, basal forebrain, medial temporal lobes and underlying parietofrontal white matter. As Ganpule et al. (2017) et al. have shown in their modelling approach, there are three broad anatomical categories that can be conceptualized when thinking about neural strain associated with concussion: 1) projection fibers (cortex–spinal cord, cortex-brainstem, and cortex–thalamus connections), 2) association fibers (cortex–cortex connections, limbic system tracts), and 3) callosal fibers (right–left hemispheric connections, p. 2159). The characteristic pathology is a toroid—an elliptical ring involving deep white matter and white-gray matter interface. In concussion, the brainstem displacement by definition has to be minimal, where pathology may actually be the most subtle. If that were

Fig. 1. Taken from Hernandez et al. (2015) based on measured head impacts and finite element modelling depict the regions of peak strain effects in a sample of athletes assessed with instrumented mouth guards that permitted extraction of regions where peak principle deformation occurred in SRC associated with positive loss of consciousness. Note in this sample the greatest deformations occur in the region of the corpus callosum, white matter underlying the cortical mantel, medial temporal lobes, basal forebrain and upper brainstem. Compare these findings with the illustrative characterization depicted in Fig. 3. Used with permission from Springer.

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Fig. 2. Schematic from Ropper and Gorson (2007) depicting where the greatest movement occurs in a midsagittal image of the brain in response to acceleration-deceleration associated with head impact. It should be noted that this depicts simple angular deceleration with the head impacting a fixed object, whereas in typical SRC concussion there is often impact from a collision while the athlete is moving with secondary and tertiary impacts as the athlete impacts other players or objects as well as hitting the ground. Accordingly, the impact forces are often more complex than what is depicted in this illustration. Reproduced with permission from the New England Journal of Medicine.

Fig. 2, and previously mentioned in the finite modelling studies, are indeed the same regions associated with pathological DTI findings in mTBI in Fig. 3. It should be noted that in the Zhou study, these mTBI participants were not specific to SRC and had a diverse etiology as to mechanism of injury. Nonetheless, from the FE depiction of where the greatest strain occurred in athletes sustaining mTBI in the Hernandez et al. (2016) investigation (Fig. 1), the Roper and Gorson Fig. 2 illustration and the DTI map of abnormal diffusion by Zhou (2016) and the modelling by Ganpule et al. (2017) that incorporates DTI, all point toward a convergence of pathological changes occurring in areas predicted to be most vulnerable in SRC. Given the assumption that any level of brainstem pathology in sport-related mTBI has to be subtle, there is another perspective potentially important in understanding structural neuroimaging findings in SRC, and that is the evolutionary significance of concussion at a brainstem level and associated neural organization of pathways associated with arousal, consciousness and motor control. Evolutionarily, to escape momentary threat and danger at the time of injury, mild brainstem deformation from a concussion that induced motor impairment would have to be transient and quickly recoverable. Indeed, this could potentially be the basis for some of the genetic associations reflective of resiliency to TBI (Dretsch et al., 2016; Merritt and Arnett, 2016; Merritt et al., 2016). Interestingly, any residual subtle upper brainstem/midbrain dysfunction that induced an ocular movement abnormality prior to the modern age may not have been as important of an issue with the less cognitively complex cognitive tasks and

These coarsely assessed, acute indicators of SRC by definition have to be brief because if they were to persist, something other than mild brain injury would have occurred. However, interesting to note that in the Darling et al. (2016) finite modelling of head impact while wearing a helmet, midbrain and brainstem regions displayed even greater displacement than the corpus callosum, which may be directly related to the number of ascending-descending fiber tracts of these mid-and hindbrain regions as opposed to the side-to-side lateral projections that dominate the corpus callosum. Understanding that these regions are vulnerable during the initial injury phase, means that they are potentially important in understanding where there may be tell-tale signs of injury related pathology associated with TBI that can be detected with advanced neuroimaging. However, the same argument applies to neuroimaging as with LOC, namely that any sports-related pathology visible on standard clinical neuroimaging will most likely meet criteria for more severe TBI pathology, and not mild TBI (Bigler and Orrison, 2004). However, as will be shown in the quantitative MRI analyses of this review, advanced neuroimaging methods have the potential to illuminate subtle brain pathology in these regions not detected with standard clinical neuroimaging and how neural networks that underlie brain structure and function may be altered in mTBI (see also Dall'Acqua et al., 2016). For example, the recent study by Zhou (2016) as shown in Fig. 3, demonstrates where white matter pathology was detected with DTI – to be described in more detail under the advanced neuroimaging section in this review. Note that the very areas that are schematically shown in 4

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Fig. 3. Using a DTI method referred to as tract-based spatial statistics (TBSS) from the FMRIB Software Library (FSL, http://fsl.fmrib.ox.ac.uk/fsl), which generates a white matter “skeleton” comparing the fractional anisotropy (FA) metric between those with mTBI and controls, the study by Zhou (2016) shows the characteristic white matter regions affected, including the frontal and temporal lobes along with the corpus callosum as visualized in this TBSS plot. A: Coronal view. B: Sagittal view. C: Axial views. D: Globally, mTBI patients showed reduced FA-DAI (p = 0.038) and FAwhole brain (p = 0.035) compared to controls as displayed in the histograms (lower right). Note the confluence of observations across Figs. 1–3 for involvement of corpus callosum and other white matter regions in a frontotemporal distribution. Used with permission from the International Society for Magnetic Resonance in Medicine and Wiley.

“seen” with traditional approaches to image analysis. As shown in Fig. 2, within this so-called ‘cone of vulnerability’ to TBI from trauma involving the head, there is yet another factor critically important in the explanation of why some regions become affected and others not. This has to do with the orientation of white matter tracts in the brain, their direction of tract projection and the orientation of how the head and brain move in response to trauma, as was introduced above. Aggregated white matter tracts of the brain – fasciculi – can be identified and segregated using a DTI technique referred to as tractography (for explanations, see Bigler, 2016b; Bigler et al., 2016), with some of these white matter tracts depicted in Fig. 4. As viewed in this illustration, which is actually of the author's brain, each strand (also referred to as a streamline) is comprised of thousands, to tens of thousands of axons that are coursing together in some unified way. The colors in Fig. 4 are important as they reflect tract orientation. Blue represents vertically oriented tracts, warm colors (orange-red) reflect laterally (side-to-side) projecting tracts and green characterizes tracts oriented in the anterior-posterior plane. What is particularly important about Fig. 4 is understanding the complex array of projecting tracts, their interwovenness and to appreciate that the movement and distortion occurs in a multifactorial fashion. Referring back to Fig. 2, with just simple linear forward motion, the white matter tracts would be stretched forward, but abruptly compressed once brain parenchyma strikes the inner table of the skull, followed by rebound effects. Viewing Fig. 4 from this perspective, it becomes relatively straightforward to understand how these fiber tracts differ in orientation and that how and where they stretch, depending on the force and movement dynamics of the head injury will define where pathology is most likely to occur. Fig. 5, also from Hernandez et al. (2015) depicts a sports-related head impact in a college American football player. Note the multifactorial movement of the head within 75 milliseconds of impact, which is even prior to the head striking the ground. There are angular forces along with rotational forces occurring in conjunction with accelerationdeceleration changes, all of which are stretching, twisting and

environment of that era. For example, the complexities involved in visual scanning and saccadic movement necessary for cognitive processes like reading or operating complicated equipment would not necessary have interfered with basic visual processing in a hunter-gatherer era. Indeed, reading script only emerged as a more universal human activity after the Industrial Revolution (van Kleeck and Schuele, 2010), so while motor recovery from brainstem stretching has an evolutionary backdrop of millions of years, recovery of eye movements for complex cognitive processes like reading would not. This may be why some research is now suggesting the important role of detecting irregular eye movement in understanding mTBI (Ventura et al., 2016), as assessing ocular motility provides a technique to more directly assess brainstem networks, which simply cannot be achieved with any existing neuropsychological measure. In addition to the vulnerability of the upper brainstem to stretch and deformation in TBI, what is also illustrated in Fig. 2 are other regions that could be described as being in a ‘cone of vulnerability’ to injury from trauma involving the head. These regions include the pituitary, hypothalamus, thalamus, basal ganglia, corpus callosum and cingulate gyrus along with deep white matter of the brain. In terms of cortical surface, Fig. 2 depicts that injuries to the anterior and inferior frontal and temporal areas are most likely (see Bigler, 2007), but as shown by Ganpule et al. (2017) the timing of when each of these ROIs is injured differs but all happens within milliseconds to seconds post initial injury (Martin, 2016). Traumatic lesions may occur in any of these regions, but may take time to evolve as secondary injury effects emerge. As will be described later in this review, often pathology in these areas is only detected through quantitative changes that can be measured with various MR metrics including diffusion properties, volumetrically or in some kind of thickness, surface area, shape or contour analysis. In other words, the pathology does not produce a traditional visible ‘lesion’ but rather alters the morphology of the structure being examined and/or the connectivity between structures. Thus important to point out that such abnormalities are not envisioned by simply inspecting the scan image, as they are all empirically derived measures that cannot be 5

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Fig. 4. DTI tractography depicting whole brain aggregate white matter projections or tracts in the brain, where each strand (streamline) or ‘tube’ represents thousands to tens of thousands of axons that are projecting together. The colors represent directionality where green is anterior-posterior, red is side-to-side and blue reflects vertical (ascending or descending) projecting tracts. Note from a side view, the dominance of anterior-posterior projecting tracts whereas viewed frontally or posteriorly, a better appreciation is gain for viewing side-to-side projections as well as the vertically oriented tracts. Arrows point to regions vulnerable to SRC. The black arrow depicted in the left hemisphere points to the arcuate fasciculus that is part of the superior longitudinal fasciculus, with the white arrow pointing to the region of the uncinate fasciculus. In the ‘Front’ view, the white arrow points to the corpus callosum and the black arrow points to the vertically oriented ascending and descending, sensory and motor tracts. The black arrow on the right, viewing the DTI projections from the back of the head, depicts the midbrain, cerebral peduncle region. In SRC the perturbation of this network of tracts is what alters brain function, where these different tracts are momentarily displaced based on the movement energy created by impact and acceleration-deceleration forces as depicted in Figs. 1, 2 and 5. The DTI images generated for this illustration are from the author's brain.

Fig. 5. In the study by Hernandez et al. (2015), a collegiate American football player lost consciousness after sustaining a head impact during a regular season game. (A) Broadcast footage at 40 frames s− 1 are compared with an (B) animation of head position and orientation during the impact calculated by integrating (C) device measurements of translational acceleration and (D) rotational acceleration. Note as depicted in this illustration, there is not a single movement of the head (and ergo, the brain) but multiple directions and forces at play that occur with a few milliseconds of impact. Reproduced with permission from Annals of Biomedical Engineering.

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Fig. 6. The coronal image on the left is from the Bigler et al. (2013) investigation where the darker blue shows where white matter volume loss using a voxel-based deformation technique comparing children with severe TBI to those with orthopedic, but no brain injury. The reason for showing this illustration is that with more severe brain injury, the ROI's shown represent where the greatest shear-strain effects created white matter differences in the TBI children. As shown in this coronal image on the left, volume loss occurred throughout the white matter, but in particular within regions of the superior longitudinal fasciculus (SLF), inferior occipitotemporal or inferior longitudinal fasciculus (ILF) and the corticospinal tract (CST). The middle illustration is a dorsal view of the author's brain using DTI to extract certain tracts: red = forceps minor of the corpus callosum, turquoise blue = forceps major of the corpus callosum, yellow is a combination of the inferior occipitofrontal fasciculus (IOFF denoted by black asterisk) and the ILF (denoted by red asterisk), green = cingulum bundle, blue = corticospinal and spinothalamic tracts. Note the different trajectories and orientations of these tracts in the brain, which will move differently depending on the impact. The far right image is a side-view of the same tracts, with the same color code. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

most likely be stretched and injured, but not beyond repair if the patient recovers from trauma induced coma, the region of the CST/STC would show the greatest change in the brain along with deep white matter tracts. This is also shown in Fig. 6 (left panel), which depicts volume loss in these regions in more severe TBI. Accordingly, with lesser injuries common to SRC, these CST/STC tracts and deep white matter regions are likewise vulnerable, just not to the same degree as would be the case in more severe injuries. In viewing Figs. 4, 6 and 7, it is important to recognize that these tracts form various integrated networks in the brain critical for all aspects of human cognition and behavior, where the consequences of tract damage reflect how, which neural systems and in what ways different tract damage influences outcome. Axons can tolerate some stretching, but when modeled with FE-MR, DTI axonal stretch > 15% begins to predict injury (Sahoo et al., 2016; Singh et al., 2016a), where greater strains predict cytoskeletal damage at a minimum (von Holst and Li, 2013). Using in vitro models, Yu et al. (2009) demonstrated an 8% stretch of a hippocampal neuron was sufficient to alter electrophysiological properties of the cell. To demonstrate the importance of an integrated understanding of this network damage, one of the most common networks to be affected in TBI, including mTBI is the default mode network (DMN). The reason for this is that the DMN involves connectivity between the parietal, frontal and temporal lobes along with integration across the two hemispheres via callosal tracts (Abbas et al., 2015a; Scheibel, 2017). The complexity of this neural system is depicted in Fig. 7. Referring back to the FE-modelling illustrations (Fig. 1), and DTI-derived pathways (Figs. 4, 6 and 7), significant brain movement will distort certain aspects of the DMN network most likely in EVERY case of SRC; however, which particular region within the DMN affected would depend on the biomechanics of that injury along with individual skull and brain morphology. Regardless, disruption of just a single aspect of this network, anywhere within the network, has the potential to physiologically perturb the entire system. It is disruption of this network that likely relates to SRC symptoms of amnesia, altered attention, problematic memory, diminished alertness, so-called ‘foggy-headedness’ as well as mood dysregulation (Bigler, 2016a). As shown in Fig. 7, much of the DMN is comprised of tracts that project in anterior-posterior and lateral planes, but not vertical.

deforming the tracts visualized in Fig. 4. So depending on the momentto-moment deformations that occur in the brain will determine which of the networks are injured. From what is shown in Fig. 6 (see arrows in the Figure), using structural neuroimaging, specific tracts can be identified with DTI methods (refer back to Fig. 4 as well) and extracted to demonstrate their unique trajectory, as shown in Figs. 6 and 7. For example, the corticospinal, spinothalamic and thalamocortical tracts (SCT and STC tracts, respectively, represented in blue) as shown in Fig. 6 are vertically oriented, whereas the inferior occipitofrontal (IOFF) and inferior longitudinal fasciculi (ILF), combined in yellow, course in an anteriorposterior plane lateral to the CST and STC tracts, where the anterior (genu, shown in red) aspect of the corpus callosum bend forward on either side of midline, whereas the posterior aspect (splenium, shown in aquamarine) bends backward and the cingulum bundle (green) courses in an anterior-posterior plane. Not shown in this figure is the superior longitudinal fasciculus (SLF) because it is the largest anterior-posterior oriented fasciculus in the dorsal plane and, if shown in the same tractography figure, it would obscure the other tracts in the illustration. The general configuration of the SLF is shown in Fig. 5 (black arrow, left lateral image). In the top-down dorsal view (middle, Fig. 6), it is straightforward to visualize that the ILF and IOFF course more laterally than the cingulum bundle and therefore if the brain where rotating on its axis, the potential exists for greater stretching on the periphery of movement depending on the biomechanics of impact, thereby having a greater influence on the ILF/IOFF tracts (Martin, 2016). Remember the toroid distribution of movement associated with concussion as shown by Ganpule et al. (2017), favors the periphery of the white-gray matter interface. On the other hand, because of the position of the falx cerebri and how the anterior (genu) and posterior (splenium) aspects of the corpus callosum bend forward (forceps minor) and backwards (forceps major), respectively and their intermingling with cingulum bundle projections, these regions of the brain are particularly vulnerable to any movement that distorts the falx cerebri across midline (Bigler, 2007, 2008). Returning to the Ropper and Gorson illustration in Fig. 2, but understanding the verticality of the CST and STC tracts as opposed to all other tracts in the brain and that with severe injury, these tracts would 7

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Fig. 7. Viewing the multifaceted projections that form what is referred to as the default mode network (DMN) makes it more straightforward to understand the vulnerability of these longcoursing tracts to the effects of concussion. As in Figs. 4 and 6 this figure, adapted from Bigler (2016a), utilizes the author's MRI-rendered 3-dimensional brain to structurally depicted the ROIs of the DMN. The top left shows in mustered color the general frontal, parietal and medial temporal lobe regions that participate in the DMN. The left hemisphere has been removed with the subcortical structures color-coded as follows: Green = Caudate, Turquoise Blue = ventricle, Gray = Thalamus, Yellow = Hippocampus, Purple = Amygdala. The arrows connecting the right frontal and parietal lobes occurs via aspects of the superior longitudinal fasciculus (SLF) but the projection from the right cuneus/pre-cuneus region of the parietal lobe projects across the posterior corpus callosum, interfaces with its left parietal lobe SLF counterpart but then also engages networks that project downward to the medial temporal lobe. To best understand this illustration, the reader should also consult Fig. 6. In Fig. 6, the white star in the right panel depicts part of the projection from parietal lobe to the frontal lobe. Returning to Fig. 7, the middle panel superimposes the right hemisphere tractography color maps (excluding cortical U-fibers, see Fig. 4 for explanation of color) showing the complex intertwining of white matter projections. A dorsal view is presented in the right panel showing the lateral projecting callosal fibers at midline, but the anterior-posterior projecting fiber tracts of the cingulum bundle (black arrows). From the dorsal view, within the mid-aspect of the left hemisphere one can visualize vertically projecting tracts (blue) intermixed with lateral and anterior-posterior projecting tracts. Cortical U-fibers and the much of the SLF were eliminated from this view so that the callosal and cingulate tracts could be visualized. The bottom network illustrations are from Caeyenberghs et al. (2016) and represent a hypothetical disruption of network integrity as a result of TBI. The black and white asterisks in the control reflect long coursing, efficient single anterior-posterior connections, also interfaced with both anterior and posterior interhemispheric connections in the control illustration. In contrast, the long coursing and interhemispheric connectivity is disrupted and less efficient in the TBI sample. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

the efficiency of the network and likely slows neural processing. Water diffusion properties can be measured using fractional anisotropy or FA, along with a variety of other diffusion metrics, extracted from the DTI scan, as already introduced. In biological tissue, water diffusion is constrained by membranes and a normal healthy balance between intracellular and interstitial fluids. An FA value where there is no constraint and water molecules are free to move without membrane restriction is 0.00, whereas when totally constrained, it is 1.0. Healthy axons within a DTI-defined ROI have intermediate FA values, where healthy axolemma maintain a normal balance between intra- and extracellular water. When axons are damaged as a consequence of TBI, lower FA may be detected using DTI, if the abnormality becomes regionally aggregated. Interestingly, elevated FA may be a sign of inflammation or some form of tissue adaptation following injury (Begonia et al., 2014; Herrera et al., 2016; Robinson et al., 2016). Fig. 8 from the study by Churchill et al. (2016) involving SRC shows the familiar picture of what has been reviewed up to this point indicating the vulnerability of the corpus callosum and other white matter regions in athletes with a history of concussion. What is interesting about this figure is that these college athletes were assessed during the chronic stage, a little more than two years past their last concussion. Slightly increased FA may reflect subtle neuroinflammation, which is a factor now receiving considerably more research interest concerning white matter injury

Returning to Figures 1and 2 in this heuristic example, with acceleration-deceleration forces that move the brain forward and downward as the front of the head impacts a hard, immobile surface, the CST would be stretched vertically but note the number of pathways as depicted in Figs. 4, 6 and 7 are not vertically oriented. What this implies is that alterations in level of alertness or consciousness are likely affecting more vertically oriented tracts whereas symptoms associated with altered memory and cognitive functioning occur from disruption of DMN tracts that are mostly oriented in anterior-posterior and lateral planes. Once impact occurs, the brain comes under numerous directional forces (see Fig. 5) that relate to its mass, where initial and subsequent impacts occur, influences of translational and rotational forces along with angular velocity, radial stress caused by centrifugal forces as well as tangential shearing forces (Monea et al., 2014). This is a complex menagerie of stress/strain forces occurring within milliseconds, so how individual axons are influenced by this movement and deformation will define where initial parenchymal and network injuries occur, and ultimately what the neurocognitive and neurobehavioral outcomes may be. Fig. 7 also presents a network schematic adapted from Caeyenberghs et al. (2016) that depicts network inefficiency associated with brain injury where long-coursing and interhemispheric projections are most affected, such as those that make up the DMN. The resulting disconnections when longer coursing pathways are disrupted, reduce 8

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Fig. 8. Voxel-wise differences in MRI measures associated with prior history of concussion from the Churchill et al. (2016) investigation. Note the similarity of involved regions showing differences in DTI, consistent with the plausible vulnerability in SRC of the white matter ROIs (refer to Figs. 1–3) depicted in this illustration.

3. Observable neuropathological imaging findings associated with sports-related brain injuries

associated with concussion (Singh et al., 2016b). Meier et al. (2016b) also found increased FA in athletes with SRC at approximately one day, one week and one month post injury. What is shown in this figure represents group data, but there are also efforts to show these kinds of abnormalities for the individual athlete who has been injured (Koerte et al., 2012; Kulkarni et al., 2015). Using advanced neuroimaging directed to assessing the individual, represents the implementation of what is referred to as precision medicine, with implications for classification and diagnosis of mTBI related pathology (Lepage et al., 2017; Shaker et al., 2017). What is important about the DTI approach to structural imaging is that some inferences can be made about the microstructure of the brain (Caeyenberghs et al., 2011), and whether there is an alteration or degradation of white matter integrity. This is understandable because observable pathology like a hemorrhage or focal encephalomalacia typically have to exceed several millimeters to be reliably detected with simple visual inspection, whereas pathology that damages or disrupts axon membranes, induces an inflammatory reaction and/or alters cytoarchitecture occurs at the micron and nanometer levels, far below the ability of the naked eye to perceive a difference using conventional neuroimaging. As such, the DTI technique still with limitations holds considerable promise for detecting and identifying what is assumed to be microstructural pathology associated with SRC that is present in otherwise “normal” appearing parenchyma with conventional imaging. There is another important issue from what is depicted in Fig. 8. The subject participants in the Churchill study that characterized Fig. 8 came solely from SRC in athletes playing contact sports that included hockey, lacrosse, rugby, soccer and American football whereas the subject participants that formed the illustration shown in Fig. 3 from Zhou (2016) were recruited from a Level I trauma center and assessed generally within a month post-injury. All types of injury mechanisms were present in the Zhou participants, including motor vehicle, assaults, falls and other types of blunt force trauma. In athletes not only are they physically fit at the time of injury, but they also have some preparedness and anticipation for impact. Additionally, for some of the aforementioned sports, helmets and other protective gear is used. Accidental head injury most often occurs without any warning or preparedness for impact and without any protective gear. So the mechanisms of injury differ greatly between SRC and non-SRC blunt force, acceleration/deceleration injuries. Accordingly, it is not unexpected that certain neuroimaging patterns would differ by etiology of injury. Nonetheless, what is particularly important about all of the illustrations up to this point is the commonality of where white matter abnormalities occur in these different studies even with different mechanisms of injury regardless of whether the source was SRC or other types of mTBI. Although not common, and as already stated, there are regions where likely pathology is visibly identifiable when present in conventional neuroimaging studies. Before discussing what involves more sophisticated image analysis methods, what may be directly observable in a scan image will discussed next.

As already stated, the overwhelming majority of head injuries that occur in sports-related activities do not exhibit any visible abnormalities based on standard, conventional neuroimaging. Indeed, there have been five meetings of International Conference on Concussion in Sport in Zurich, Switzerland that have rendered consensus opinions on sportsrelated concussion where the general conclusion has been “… that no abnormality is seen on standard structural neuroimaging studies (p. 250, McCrory et al., 2013).” This is not to say that there are not potential major abnormalities and even life-threating TBIs that occur from sport-related head injuries where specific clinical guidelines exist for when clinical imaging is appropriate in sports-related injuries (Amyot et al., 2015). This consensus statement is merely indicating that in general, sports related concussion is not associated with identifiable pathology on clinical neuroimaging. The fundamentals of neuroimaging of TBI have been reviewed elsewhere (see Bigler, 2015; Wilde et al., 2012). For additional background the reader is referred to those publications and other textbooks on the topic. The current review assumes some familiarity with CT and MRI terminology and techniques. As presented in Table 1 from the review by Morris et al. (2014), the major features of clinically observable abnormalities in sports-related injuries on CT or MRI are in the form of different types of hemorrhages including contusion, subarachnoid, subdural, epidural and petechial hemorrhages, along with edema and associated abnormalities as well as skull fractures. Abnormalities of white matter, particularly observed on the fluid attenuated inversion recovery (FLAIR) sequence and prominence of Virchow-Robin spaces, also referred to as dilated perivascular spaces, have been reported (Bigler et al., 2016; Bigler and Maxwell, 2012; Inglese et al., 2006; Orrison et al., 2009; Sharp and Ham, 2011), but the specificity of these types of abnormalities to TBI remains an issue (Jarrett et al., 2016). Examples of most of the clinically visible lesion/abnormalities observed in sports-related head injury are summarized in Fig. 9. Possibly the most clinically significant findings indicative of shear injury is hemosiderin deposition, also referred to as ‘microbleed(s)’ or ‘microhemorrhage(s).’ This is particularly important because in the healthy brain, especially under 50 years of age, detection of the hypointense signal characteristics of hemosiderin deposition in brain parenchyma is rare. So its presence in a young athlete who was otherwise healthy, with no cardio- or cerebrovascular risk factors and only the SRC, is most likely related to the brain injury. However, while inspection for hemosiderin deposition should be part of any MR examination of the athlete who undergoes MRI, it is uncommon to find hemosiderin deposition in sports injury. Some of this may be related to the MRI method used as susceptibility-weighted imaging (SWI) has been shown to be superior to the traditional gradient recalled sequence (GRE). Since the SWI sequence is relatively new, past research may have under reported the true prevalence of hemosiderin deposition in sports-related head injury. Nonetheless, in 100 combatants (boxers, mixed martial arts, judo, etc.) examined by Orrison et al. (2009), no 9

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Table 1 Classification of Traumatic Brain Injury in SRC from Morris et al. (2014)*.

Focal

Injury type

Pathophysiology/Biomechanics

Clinical sequelae

Epidural hematoma

May exhibit lucid interval Arterial bleeding source, so can rapidly deteriorate Good neurological prognosis

Intracerebral hemorrhage (contusion, bruise) Traumatic subarachnoid hemorrhage Concussion

Direct blow (i.e. baseball) Often nonhelmeted players Often associated with skull fracture and middle meningeal artery injury Usually no underlying brain injury Tearing of bridging veins As opposed to elderly population, less subdural space, so expansion Leads to rapid increase in ICP Primary neuronal or parenchymal vessel injury caused by direct blow or acceleration/deceleration injury Injury of surface vessels Cortical location, underlying parenchymal injury Mild TBI

Malignant cerebral edema (brain swelling)

Most common in younger athletes Diffuse cerebral swelling

Diffuse axonal injury

First-degree injury; rotational forces causing axonal shearing Force greatest at areas of highest tissue density (gray-white junction)

Subdural hematoma

Diffuse

Continued progression of symptoms; focal findings on examination common Usually underlying brain injury Potential to progress or blossom first 24–48 h Similar to brain contusion Common in severe TBI Loss of consciousness not a requirement Most common clinical complaints: headache, dizziness, memory loss Classically associated with second impact syndrome Rapid progression to fatal ICP secondary to hyperemia and vascular engorgement Often lower GCS score and permanent neurological deficits

* GCS, Glasgow Coma Scale; ICP, intracranial pressure; TBI, traumatic brain injury

had large CSP as shown in Fig. 10 (see also Casson et al., 2014). In a smaller sample of retired NFL players some level of CSP was observed in 16 of the 17 examined with MRI (R. C. Gardner et al., 2016). Of course, if sequential scanning is available where pre-contact sport or pre-season imaging could establish a baseline, then development of CSP would be a sign of injury, as shown in Fig. 10. Development of CSP is thought to relate to altered pressure gradients by trauma-induced shifting of ventricular CSF that impacts the septum pellucidum, the delicate membrane that divides the two lateral ventricles at midline (Aviv et al., 2010). Years ago, based on clinical reading of head CT scans, Jordan et al. (1992) reported that boxers with CSP had more cortical atrophy, but no similar study has been performed with MRI, where it would be critical to have pre-injury or some sort of baseline scan, to monitor CSP changes over time. Most recently, Koerte et al. (2016b) reviewed the literature of CSP in chronic traumatic encephalopathy (CTE), showing a high rate of occurrence of CSP in post-mortem studies and then examined for CSP in 72 former NFL players compared to non-contact, agematched athletes, also observing a higher rate of CSP in the retired football players. Of recent, CTE is one of the most discussed and debated sports related disorders (Turner et al., 2016) but does not have an established neuroimaging signature although some of the neuroimaging techniques discussed in this review are being examined in CTE, where some of the pathologies described above are considered to be factors (Hay et al., 2016; Smith et al., 2013). Although there have been a small number of neuroimaging studies in those suspect with CTE (Shetty et al., 2016; Sundman et al., 2015), there are no definitive conclusions at this time or prospective neuroimaging signs to diagnose the condition. While the above structural neuroimaging findings may be observed in sports-related TBI, the relationship of such findings to treatment and clinical outcome is one of active investigation. As of this review, with negative structural neuroimaging findings it is likely that the clinician should just follow standard return-to-play guidelines; however, with presence of positive conventional neuroimaging findings, such observations raise potential concerns over career ending findings, implicating presence of definite structural pathology associated with SRC (Doolan et al., 2012; Hobbs et al., 2016; Toth, 2015). Someday, it may be that advanced neuroimaging findings will play a critical role in return-to-play decisions (Newsome et al., 2016), but the science is not to that stage as of this writing (see Broglio et al., 2017).

microbleeds were identified. In contrast, Hasiloglu et al. (2011) did observe two out of 21 amateur boxers had microbleeds, using SWI. In a pediatric sample (children 9–15 years of age) with mTBI as assessed in a Level I trauma center at a regional Children's Hospital only those with falls and head injuries associated with some form of a motor vehicle accident had hemosiderin detected using an SWI sequence; none of those with sports-related injuries had hemosiderin findings (Bigler et al., 2016). Also in a pediatric sample of SRC, in 151 cases examined, Ellis et al. (2015) found a microbleed in only one individual who underwent MRI. Jarrett et al. (2016) examined 40 collegiate ice-hockey players over a season and none were found to have a microbleed, although those who were concussed had white matter hyperintensities (WMHs, to be discussed later) closer to the gray-white matter junction than controls. White matter abnormalities close to the gray-white matter junction are thought to be those more associated with shearstrain effects on brain parenchyma (see Orrison et al., 2009, p. and Fig. 9I), due to tissue tolerance differences between gray and white matter. Casson et al. (2014) examined 45 retired National Football League (NFL) players 30–60 years of age where 4 (9%) had identifiable hemosiderin deposition detected on SWI. Interestingly, presence of SWI findings were related to the number of concussions or “dings” (as defined in the study) as were DTI findings, but these clinical neuroimaging findings did not systematically relate to a specific pattern of neuropsychological impairments (also see Kuhn et al., 2016). In contrast, Hart et al. (2013) in an older sample of retired NFL players did observe relations between white matter findings, cerebral blood flow and neuropsychological impairments on tasks of memory, naming and word finding. Similarly, Strain et al. (2013) reported that depressive symptoms were related to white matter DTI findings in retired NFL players. Similarly, in a Canadian study by Multani et al. (2016) that examined retired professional football players, DTI abnormalities were observed in those with a history of multiple concussions that related to depressive symptomatology as well as cognitive functioning. In professional boxers, multiple DTI studies have shown presence of abnormal DTI findings (Chappell et al., 2008; Chappell et al., 2006; Shin et al., 2014; Wilde et al., 2016) that have been replicated in amateur boxers as well (Herweh et al., 2016). Cavum septum pellucidum (CSP) was considered one of the original pathologies associated with boxing (Bogdanoff and Natter, 1989), which may still be the case (see Aviv et al., 2010), but CSP occurs in healthy control samples and therefore lacks specificity. In 45 retired NFL players, Kuhn et al. (2016) observed that 71% had small and 7% 10

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Fig. 9. (A) Intraparenchymal hemorrhage depicted in axial CT one-hour post injury from being struck by a hockey puck during a professional ice hockey game in a 27-year-old athlete. Used with permission from Degen et al. (2016). (B) Curvilinear hypointense signal (GRE) abnormality within the posterior deep white matter adjacent to projections from the splenium of the corpus callosum reflective of hemosiderin deposition and prior intraparenchymal hemorrhage in an 11-year-old hockey player who fell and had positive LOC. Used with permission from Ellis et al. (2015). (C) Susceptibility-weighted imaging defined microbleed within the deep white matter of the frontal lobe (red arrow) in a 32-year-old professional football player. From Casson et al. (2014) used with permission. (D) 67-year-old struck in the head by a stray golf ball, who experienced brief loss of consciousness with CT demonstrating intracerebral hematoma and surround edema but no skull fracture. From Etgen and Sander (2008) used with permission. (E) Depressed skull fracture from being hit by a shot put in a 13-year-old athlete. Used with permission from Ibrahima and Motah (2015). (F) While playing a ringette ice game, this 14-year-old fell striking her head but with no LOC. MRI demonstrated what was interpreted as a nonhemorrhagic parenchymal contusion. Used with permission from Ellis et al. (2015). (G) linear skull fracture in 3-D CT reconstructed skull in the hockey player shown in A. From Degen et al. used with permission (H) Axial CT in a football player with frontal subdural hematoma. From Treister et al. (2014) used with permission (I) White matter hyperintensity (WMH) in frontal white matter in an unarmed combatant (either mixed martial arts or boxer). From Orrison et al. (2009) used with permission. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4. Advanced quantitative neuroimaging analyses in sports-related head injury

namely a frontotemporal, deep white matter and corpus callosum pattern (Ganpule et al., 2017). Combining those observations with FE modelling of mTBI, if significant deformation-strain pathologies occur at cellular levels within this presumed distribution of likely pathology in sports-related TBI, the assumption is that such pathologies may alter the size, shape, volume or some other physical metric including

Recalling the ‘cone of vulnerability’ depicted in Fig. 2 and as reviewed above, visibly detected pathologies that occur in sports-related brain injuries follow the same distribution as in non-sport mTBI, 11

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Fig. 10. Cavum septum pellucidum (CSP) abnormalities associated with SRC as derived from Gardner et al. R. C. Gardner et al. (2016) are depicted in this illustration. Cavum septum pellucidum (CSP) grading diagrams and CSP grade and length for each patient. (A) The initial measurements that were taken in the midsagittal plane. The solid horizontal line depicts the longest intraventricular distance from genu to splenium of the corpus callosum (termed “septal length”). All images were then reformatted such that the coronal axis was perpendicular to this line to optimize coronal measurements along the septum. The dashed vertical line depicts the most anterior aspect of the columns of the fornix. This line was used to distinguish between the pre-fornix septum (where separations of the leaves of the septum pellucidum are termed CSP) and the post-fornix septum (where separations of the leaves of the septum pellucidum are termed “cavum vergae” [CV]). (B) A reformatted coronal MRI image for one patient that shows the greatest evidence of separation of the leaves of the septum pellucidum. This view was used to perform CSP grading for each patient. The dashed box depicts the region that is then enlarged to illustrate “Grade 2” in panel B. (C) Enlarged views of representative T1 coronal images for each CSP grade. Note that the Grade 0 septum appears crisp without any evidence of cyst (CSP absent). Grade 1 septum shows slight interior hypointensity that is not, however, clearly CSF signal intensity (septum unclear/CSP equivocal). Grades 2–4 show clear evidence of CSF signal between the separated leaves of the septum pellucidum. The degree of separation between the leaves of the septum pellucidum is then used to assign a grade of 2–4: Grade 2 CSP is not wider than the septum, Grade 3 CSP is wider than the septum but less than half the intraventricular width, and Grade 4 CSP is greater than half the intraventricular width. (D) CSP grade and length for each patient. Manual horizontal jitter was added to overlapping “grade” values in the graph to improve visibility.

network analyses of a given brain structure or ROI, even though conventional abnormalities may not be visible (Churchill et al., 2017a; Churchill et al., 2017b; Palacios et al., 2017; van der Horn et al., 2017). Based on this assumption, a considerable research effort has been underway for the last decade to examine structural imaging in sports-related head injury using advanced methods for quantitative image analysis (see Talavage et al., 2015). In general, these advanced quantitative efforts involving structural MRI have followed either a brain morphometry approach, examined DTI metrics and recently, examined structural bases to network integrity (Bigler, 2016b). Morphometric analyses of structural MRI findings typically begin with segmenting (separating) brain parenchyma from cerebrospinal fluid, meninges and skull, and then between white and gray matter and cerebrospinal fluid (CSF), followed by classifying (labeling) these images either anatomically and/or by some ROI designation (Bigler, 2015). An example of this, using the FreeSurfer® (https://surfer.nmr. mgh.harvard.edu/) technique, is shown in Fig. 11. The FreeSurfer® automated pipeline corrects for motion, normalizes the voxel intensity throughout the image, removes non-parenchymal volume through use of a skull stripping procedure, registers each subject to a standard template space, performs pial surface correction, and segments cortical and subcortical regions. What used to take hours to perform by hand has now become automated. This approach permits the examination of potential anatomical differences between athletes with a history of sports-related head injury compared to those without such a history or a healthy, typical developing control sample (Churchill et al., 2017b; Wu et al., 2017). As

Fig. 11. Coronal MR image showing a segmented and classified image that isolates various ROIs and actual structures with a color-coded schematic based on FreeSurfer. For example, the color classification for hippocampus is olive, thalamus a lime green, globus pallidus blue and putamen plum colored. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

presented in Fig. 11, by specifically identifying and separating the boundaries of a given structure or ROI, a variety of metrics can be applied. Some of these methods directly measure volumes or thickness, whereas other use various techniques to uniformly merge data into 12

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Fig. 12. From Meier et al. (2016c) depicting football athletes with a history of concussion (Ath-mTBI) and presence of cortical thinning in the left ventromedial prefrontal cortex (PFC) relative to healthy controls (HC; in A) and in the right motor cortex relative to football athletes without a history of concussion (Ath; in B). Violin plots containing box and whisker and kernel density plots are displayed for each cluster. (C) From Mayer et al. (2015) who examined MMA fighters and computed a “symmetrized” percent change (SPC) value for cortical thickness over the course of the study (follow-up to baseline visit) for HC (blue) and MMA (red). Box-and-whisker plots indicate increased thickness at follow-up scans for HC and decreased thickness for MMA relative to their respective baseline evaluations. HC, healthy controls; LH, left hemisphere. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

In terms of white matter changes in SRC, Herweh et al. (2016) examined 31 male amateur boxers as well as number of fights the boxers had participated in and observed widespread DTI changes within the expected ROIs, namely corpus callosum and deep white matter. This is shown in Fig. 13 – note the similarity of distribution of white matter changes detected by DTI in the Herweh et al. study compared to Figs. 3, 8 and what has been discussed in this review. Mayer et al. (2016) also examined MMA fighters showing a variety of white matter DTI findings, including within the cerebral peduncle. Mayer et al. (2015) have shown that in mixed-martial arts fighters that in addition to reduced cortical thickness, there was also reduced subcortical white matter volume. Banks et al. (2014) demonstrated that in both boxers and MMA fighters that a variety of subcortical volumetrics related to not only the number of fights but also cognitive and personality factors. Cortical thickness in young to middle aged-adults has also been shown to be reduced in those with two or more SRCs (List et al., 2015). DTI methods permit extraction of a wealth of water diffusion parameters that relate to the integrity of white matter as introduced earlier in this review. Animal studies have histologically confirmed TBIrelated systematic alteration of white matter tracts that approximate that observed in human studies (Herrera et al., 2016; Stemper et al., 2015). Accordingly, alterations in DTI parameters in SRC participants is thought to reflect aberrant white matter, although neuropathological specifics can only be inferred at this time (Dimou and Lagopoulos, 2014; A. Gardner et al., 2012). A growing number of studies show the expected corpus callosum and deep white matter DTI differences in athletes with a history of concussion (Bazarian et al., 2012; Chamard et al., 2016; Cubon et al., 2011; Gajawelli et al., 2013; A. Gardner et al., 2012). As already mentioned, there is considerable uniqueness with each injury, for example, type of sport, whether helmeted or not, sex and age differences, whether the impact was anticipated or an unexpected collision, whether impact was with another player, ground, ball, equipment or playing field structure, to name a few. Accordingly, given the uniqueness of individuals injured playing sports and how injured, injuries across DTI sports-related studies, while frequently reporting DTI findings they are not always in the same regions or

templates for group comparison (Liu et al., 2015). For example, in two studies that involved either American collegiate football (Meier et al., 2016a) or hockey players (Albaugh et al., 2015), history of concussion related to cerebral cortical thickness, although in the Albaugh et al. study the effect was interactive between age and concussion history. Consistent with the hypothesis that if sports-related TBI altered morphology, it would do so in a frontotemporal distribution was supported by both of these investigations. As depicted in Fig. 12, Meier et al. (2016c) observed that thinner cortical thickness in collegiate football players was observed in the more ventromedial region of the frontal lobe in those with a history of concussion. Sussman et al. (2017) have also shown reduced frontal gray matter volume in concussion, although their sample was not specific to sports concussion (see also Dall'Acqua et al., 2017). One of the classic areas vulnerable to TBI is the medial temporal lobe, including the hippocampus and amygdala (Bigler, 2007; Leh et al., 2016). Volumetric analyses of the hippocampus have been the target of a number of TBI studies, but only a few have been specific to sportsrelated injuries. Singh et al. (2014) observed in collegiate football players concussion history and years playing football were associated with smaller hippocampal volume. List et al. (2015) in a group of 20 young-to-middle aged participants with two or more SRCs did not observe hippocampal atrophy, but did find reduced entorhinal cortical thickness. Meier et al. (2016b) have also shown reduced hippocampal subfield volumes in collegiate football players with history of concussion. In former NFL players with a mean age of 58.1 years, Strain et al. (2015) found that prior concussion history associated with loss of consciousness was a risk factor for hippocampal atrophy and the development of mild cognitive impairment (MCI). In individuals with MCI, hippocampal volume loss appeared to be greater among those with a history of concussion. Bernick et al. (2015), in a United States based study of professional fighters, examined 224 combatants (131 mixed martial arts fighters and 93 boxers) observing that exposure to repetitive head trauma determined by number of professional fights, years of fighting or a fight exposure score was associated with lower brain volumes and lower processing speed in active professional fighters. 13

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Fig. 13. From Herweh et al. (Herweh et al. (2016)) showing widespread DTI findings of reduced FA in amateur boxers.

DTI metrics, implicating a potential complex interaction between prior concussive brain injury, repetitive head impacts, age and aging. The fact that as of the writing of this review, there are limited numbers of studies that have examined sports-related head injuries using morphometry and DTI techniques means that there is only limited understanding of what these findings mean for players. Kamins et al. (2017) and McCrea et al. (2017) in the recent 5th (2016) International Consensus Conference for Sport Concussion have reviewed this literature as well. While structural neuroimaging may have limitations in detecting clinically meaningful changes (Espana et al., 2017; Terry and Miller, 2017), neuroimaging biomarkers that tap brain chemistry like MRS or other methods may prove more sensitive (A. J. Gardner et al., 2017; Mayer et al., 2015; Shahim et al., 2016). While the results are advancing our understanding of the subtler aspects of neurological injury that may accompany sports-related head injury, there is no universal agreement on what these findings mean, how they should be used in assessing or monitoring athletes or how they can be used to track and monitor players. Along these lines an additional cautionary note has been registered by Casson et al. (2014) who examined both clinical as well quantitative neuroimaging measures in a retired NFL cohort. Their conclusions after conducting an in-depth neurological and neuropsychological assessment in conjunction with the neuroimaging was that “MRI lesions and neuropsychological impairments were found in some players; however, the majority of retired NFL players had no clinical signs of chronic brain damage (p. 384).” Additionally, although the Coughlin et al. study cited above demonstrated increased TSPO, suggestive of brain injury and repair, the NFL participants in that study as a group did not differ on neuropsychological test performance or brain volumetric ROI analyses. Lastly, as pointed out by Tarnutzer et al. (2016) in their systematic review of the neuroimaging literature involving soccer, there are shortcomings in the design of such studies, none are based on large samples, prospectively followed with appropriate baseline and tracking methods. In other word, more research is needed.

direction of diffusion (A. Gardner et al., 2012). Time post-injury also relates to DTI findings as shown by Murugavel et al. (2014) who examined longitudinal DTI changes, in particular FA reductions, and how they developed within 2 days, 2 weeks and 2 months of a concussive brain injury. Similarly, Bazarian et al. (2014) have shown FA changes over a football season in college football players related to the number of head impacts in those with no history of concussion. The implication of the Bazarian et al. investigation is that repeated head impacts may cause a perturbation in white matter integrity, which was also supported by a separate study by McAllister et al. (2014), in a group of college athletes involved in contact sports. Lipton et al. (2013) examined soccer heading in amateur players who evidenced no clinically identifiable structural MRI abnormality but increased heading was associated with reduced FA. Interestingly, there was also an association of reduced white matter structural integrity, number of soccer headings and neurocognitive test performance assessing short term memory. Davenport et al. (2014) also reported that in those athletes over a season altered DTI metrics were observed related to head impact exposure. There are also potential sex differences which may relate to differences in neck-head tolerance during impact that relate to DTI findings in athletes with sports-related concussion (Chamard et al., 2013; Fakhran et al., 2014). DTI analysis lends itself to examining neural networks and connectivity (Hayes et al., 2016). As already mentioned the DMN has been a target of several DTI network analysis and other advanced neuroimaging studies in SRC (Abbas et al., 2015b; Johnson et al., 2014; Militana et al., 2016; Orr et al., 2016; Zhu et al., 2015). Other than the CST, some of the pathways involved in the DMN are some of the longest point-to-point projecting fibers in the brain, which likely represents a vulnerability to stretch and deformation related to SRC (see Fig. 8). Lancaster et al. (2016) used a serial diffusion MRI comparison in tracking acute SRC within 24 h of the concussion and then eight days later. They used an approach referred to as diffusion kurtosis tensor imaging in association with tradition DTI measures showing that MR diffusion metrics remained altered in the concussed athletes at 8 days, even though there was symptom improvement and even resolution in some. At eight days post-injury, cognitive testing was no longer differentiating those with concussion, yet neuroimaging findings were. This suggests that the pathophysiological effects of SRC may persist beyond the typical window of presumed resolution as detected by traditional cognitive and neurobehavioral assessment methods (Kaur and Sharma, 2017). These DTI approaches have also been used to examine whether chronic exposure to head impact, such as in retired American football players relates to aging (Ng et al., 2014; Strain et al., 2013). Recently, Coughlin et al. (2016) examined four active and 10 retired NFL with MRI, including DTI and positron emission tomography (PET) using a ligand that is a marker of activated glial cell response [translocator protein 18 kDa (TSPO)]. Increased TSPO was observed in players as well as some differences from the control participants in white matter

5. Multimodality MR-based neuroimaging studies in sportsrelated head injury This review focused on structural neuroimaging findings in sportsrelated head injury. Of course, structural anatomy is only part of the equation related to brain function. Most likely, advances in understanding the subtler aspects of brain injury associated with sports, will come from the integration of neuroimaging techniques, not just improvement in advanced neuroimaging analyses of brain structure. Accordingly, what is emerging now is taking the structural information from neuroimaging and integrating with various types of functional neuroimaging, in what is referred to as a multimodality approach (Bigler, 2016b; Keightley et al., 2012; Narayana et al., 2015; Tremblay et al., 2017; Van Horn et al., 2016). In the past because of limits in image analysis and statistical approaches, research designs typically 14

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with the structural and quantitative findings may provide the most comprehensive perspective on presence of brain pathology in those athletes who have experienced a SRC. Caution is needed in interpreting these findings because professional athletes, as well as the general population may have other risk factors, which can only be assessed with larger sample sizes, studied prospectively where attempting to better control for injury and subject variability. Accordingly, the design that has not been undertaken – a large sample of prospective, longitudinally followed athletes tracked over time with a multimodality neuroimaging approach has not occurred, but we now have the capability of conducting such studies (Koerte et al., 2016a). Issues of replication are important as well (Solomon et al., 2016).

were directed to examining one or a limited number of neuroimaging variables. However, there is an incredible abundance of anatomical and physiological information in scan data, which can now be integrated within a ‘big data’ format (Van Horn and Toga, 2014). What this also permits is the use of multiple neuroimaging methods to assess brain networks and neural network integrity in SRC, rather than just a specific ROI (Hayes et al., 2016; Orr et al., 2016; Talavage et al., 2015; Vergara et al., 2016; Xue et al., 2015). For example, a variety of MR techniques provide a window in to functional changes in the brain typically based on inferring activation related to detecting blood oxygen level-dependent (BOLD) signal differences, including BOLD techniques like arterial spin labeling (ASL) to infer cerebral blood flow (CBF). For example, recently Mutch et al. (2016) used MR-based CBF techniques to demonstrate differences in cerebrovascular responsiveness to a CO2 challenge that could differentiate adolescent participants who had experienced concussion from those who had not. Important in the Mutch et al. investigation was that on clinical grounds, there were no structural MRI abnormalities in any of the participants. The Churchill et al. (2016) study previously mentioned, in addition to DTI, as already shown in Fig. 9, used a multimodality approach that included volumetrics, CBF and DTI. Subtle volume loss was noted in frontal cortical regions and cerebellum. CBF findings related to decreases in frontotemporal lobe regions and supplementary motor areas. These athletes with prior concussion presumably had no abnormalities on the conventional MR sequences and did not differ in terms of symptom reporting compared to the non-concussed control athletes. What this suggested to these investigators is that the chronic abnormal findings using a multi-modality MR approach reflected the brain's adaption as well as residual effects to the concussive brain injury. Since the mean age of these athletes was just around 20 years, how such chronic effects interplay with aging and other neurological and neuropsychiatric risk factors remains to be seen. Similarly, Slobounov et al. (2017) used a multimodality approach to assess structural brain integrity prior to season and after over a season of collegiate football, showing a variety of changes associated with the force of impact recorded by helmet telemetry. Magnetic resonance spectroscopy (MRS) may also be combined with DTI and CBF measures that relate to differences associated with sports related concussion (Bartnik-Olson et al., 2014; Gardner et al., 2017). One of the promising opportunities afforded by this integrated, multimodality approach is the assessment of interaction effects. For example, when analyzed singularly a particular ROI may not be significant but when examined in an interactive way across different neuroimaging modalities, important relations may be revealed (Bigler, 2016b), where MRS findings eventually may play a role in return to play decision making (Pan et al., 2016). As already introduced, there are now markers of glial cell activation that can be obtained with PET (Coughlin et al., 2016; Coughlin et al., 2015) as well as tau and amyloid burden (Mitsis et al., 2014). Such functional neuroimaging studies in former NFL players have raised important concerns about subtle brain injury and/or the adverse longterm consequences of repeat sub-concussive injuries (LoBue et al., 2016). Amen et al. (2016) examined a cohort of 161 retired and current NFL players and found hypoperfusion in orbitofrontal, cingulate and anterior and mesial temporal lobe regions in players that differed from controls. Age at first exposure to tackle football in retired professional football players has been reported to be associated with altered DTI white matter microstructure (Stamm et al., 2015). Bahrami et al. (2016) examined head impact via helmet telemetry in American style football and assessed 20 youth (8–13 years of age) over a season, with baseline and follow-up DTI. Despite no child clinically experiencing a concussion, there were nonetheless, DTI findings that related to the biomechanical findings, suggesting that subconcussive blows may be altering white matter microstructure. Accordingly, adding functional neuroimaging provides additional information, which when integrated

6. Conclusions Advanced neuroimaging techniques are providing unique insights into probable subtle brain pathology associated with SRC. The integration of multiple approaches to neuroimaging analysis will likely be most informative given the heterogeneity of SRC and the potential brain regions affected. Advanced neuroimaging methods will likely play an increasingly important role in the assessment and management of SRC. Acknowledgment No grant funding was used to write this review. Dr. Bigler co-directs the Neuropsychological Research and Assessment Laboratory at Brigham Young University, which does provide forensic consultation. During the writing of this review the Brain Imaging and Behavior Laboratory at Brigham Young University, which Dr. Bigler also directs was supported by funding from the Chronic Effects of Neurotrauma Consortium from the Department of Defense (W81XWH-13-2-0095) and the US Department of Veterans Affairs (I01 RX 002174). The views expressed here are those of the author and do not necessarily reflect the official policy of position of the Department of Defense, The Department of Veterans Affairs, nor the US Government. The assistance of Tracy Abildskov with illustrations and Jo Ann Petrie, Ph.D. with manuscript preparation is gratefully acknowledged. References Abbas, K., Shenk, T.E., Poole, V.N., Breedlove, E.L., Leverenz, L.J., Nauman, E.A., ... Robinson, M.E., 2015a. Alteration of default mode network in high school football athletes due to repetitive subconcussive mild traumatic brain injury: a resting-state functional magnetic resonance imaging study. Brain Connect. 5 (2), 91–101. http:// dx.doi.org/10.1089/brain.2014.0279. Abbas, K., Shenk, T.E., Poole, V.N., Robinson, M.E., Leverenz, L.J., Nauman, E.A., Talavage, T.M., 2015b. Effects of repetitive sub-concussive brain injury on the functional connectivity of Default Mode Network in high school football athletes. Dev. Neuropsychol. 40 (1), 51–56. http://dx.doi.org/10.1080/87565641.2014. 990455. Albaugh, M.D., Orr, C., Nickerson, J.P., Zweber, C., Slauterbeck, J.R., Hipko, S., ... Hudziak, J.J., 2015. Postconcussion symptoms are associated with cerebral cortical thickness in healthy collegiate and preparatory school ice hockey players. J. Pediatr. 166 (2), 394–400. e391. http://dx.doi.org/10.1016/j.jpeds.2014.10.016. Amen, D.G., Willeumier, K., Omalu, B., Newberg, A., Raghavendra, C., Raji, C.A., 2016. Perfusion neuroimaging abnormalities alone distinguish National Football League players from a healthy population. J. Alzheimers Dis. 53 (1), 237–241. http://dx.doi. org/10.3233/JAD-160207. Amyot, F., Arciniegas, D.B., Brazaitis, M.P., Curley, K.C., Diaz-Arrastia, R., Gandjbakhche, A., ... Stocker, D., 2015. A review of the effectiveness of neuroimaging modalities for the detection of traumatic brain injury. J. Neurotrauma 32 (22), 1693–1721. http:// dx.doi.org/10.1089/neu.2013.3306. Aviv, R.I., Tomlinson, G., Kendall, B., Thakkar, C., Valentine, A., 2010. Cavum septi pellucidi in boxers. Can. Assoc. Radiol. J. 61 (1), 29–32. quiz 21–22. http://dx.doi. org/10.1016/j.carj.2009.09.002. Bahrami, N., Sharma, D., Rosenthal, S., Davenport, E.M., Urban, J.E., Wagner, B., ... Maldjian, J.A., 2016. Subconcussive head impact exposure and white matter tract changes over a single season of youth football. Radiology 281 (3), 919–926. http:// dx.doi.org/10.1148/radiol.2016160564. Bandak, F.A., Ling, G., Bandak, A., De Lanerolle, N.C., 2015. Injury biomechanics, neuropathology, and simplified physics of explosive blast and impact mild traumatic brain injury. Handb. Clin. Neurol. 127, 89–104. http://dx.doi.org/10.1016/B978-0-

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