MRI-based measures of intracortical myelin are sensitive to a history of TBI and are associated with functional connectivity
Introduction
Traumatic brain injury (TBI) represents a significant medical burden both in the general population, where TBIs accounted for approximately 2.8 million emergency department visits, hospitalizations, and deaths in 2013 alone (Taylor et al., 2017), as well as in the U.S. military and Veteran population, where TBI is argued to be the signature wound of U.S. military personnel who fought in Iraq and Afghanistan (Wojcik et al., 2010). TBIs can result in deficits in learning and memory, anxiety and mood, and executive function (Agoston and Kamnaksh, 2015; Bruce, 2010; Cole and Bailie, 2016; Kennedy et al., 2010; Rosenfeld and Ford, 2010; Thompson et al., 2008; Ursano et al., 2010). While cognitive and behavioral deficits caused by “mild” TBI often (though not always) resolve without intervention (Belanger et al., 2005; Dikmen et al., 2001; Hessen et al., 2007), deficits caused by more severe TBIs may persist for years or throughout the patient's lifetime (Langlois et al., 2006; Vanderploeg et al., 2007, 2009; Zaloshnja et al., 2008).
In principle, an accurate diagnosis of TBI severity could be highly useful in predicting a patient's expected level of long-term deficit and in gaining access to rehabilitative services. However, assessments of TBI are usually not based on any neurobiological measure, but are made based on the severity of post-TBI symptoms, including the presence and duration of consciousness loss immediately after the injury, the presence of altered consciousness, and the presence of post-TBI amnesia (Corrigan and Bogner, 2007; Vasterling et al., 2008). Perhaps as a result, post-TBI symptom-based assessments are only weakly predictive of behavioral outcomes, usually accounting for less than 10% of the variance in cognitive function or quality of everyday life (Cappa et al., 2011; Chien et al., 2017; Hiekkanen et al., 2009; Rohling and Demakis, 2010). Thus, there is a significant need for more advanced diagnostic techniques that can effectively measure the impacts that TBIs have on the brain in order to deliver accurate prognoses.
In the most common case of a closed-head injury, TBI-related cognitive dysfunction is believed to be driven by the presence of diffuse axonal injury (Pearce, 2008; Silver et al., 2011). In this type of injury, the axonal projections that connect brain regions are severed and/or demyelinated by shearing forces induced by a head impact or blast wave. This axonal damage in turn is believed to impair networked brain communication, resulting in cognitive impairment (Hayes et al., 2016; Kaplan et al., 2018). These effects on myelinated axonal processes have led to extensive scientific interest in examining TBI-related reductions in the integrity of large white-matter tracts. Such investigations typically use diffusion MRI techniques that detect variations in the anisotropic diffusion of water to identify disruption in white matter regions. This extensive corpus of work broadly indicates white matter abnormalities in TBI (see e.g. Huisman et al., 2004; Kraus et al., 2007; Mac Donald et al., 2011; Rutgers et al., 2008a, 2008b; Wozniak et al., 2007; for more comprehensive reviews, see Asken et al., 2018; Douglas et al., 2015; Fox et al., 2013; Hulkower et al., 2013; Kaplan et al., 2018; Niogi and Mukherjee, 2010; Sharp and Ham, 2011; Shenton et al., 2012; Strauss et al., 2015), though the particular locations of disruption vary substantially from study to study and individual to individual, suggesting that TBI-induced white matter abnormalities are spatially heterogeneous (Davenport et al., 2012; Hayes et al., 2015; Jorge et al., 2012; Mac Donald et al., 2011; Taber et al., 2015; Wallace et al., 2018a; Ware et al., 2017). Diffusion-related measures of white matter integrity have also been consistently correlated with TBI outcomes such as clinical symptoms and cognitive function (e.g. Gordon et al., 2018; Gu et al., 2013; Kraus et al., 2007; Kumar et al., 2009; Matsushita et al., 2011; Palacios et al., 2011; Wada et al., 2012; Xiong et al., 2014; see also meta-analyses by van Eijck et al., 2018; Roberts et al., 2016; Wallace et al., 2018b).
While diffusion-related measures of white matter myelination have been of high scientific interest in TBI due to their ability to differentiate TBI groups from healthy controls and their association with outcomes, they have not successfully been linked to disruptions in networked brain communication. We previously demonstrated that diffusion measures of white matter myelination were affected by TBI, but were uncorrelated with TBI-influenced fMRI measures of brain communication (Gordon et al., 2018). Perhaps as a result, diffusion measures have not been successfully used for TBI diagnosis. For example, Mac Donald et al. (2011) used diffusion imaging to identify statistically robust TBI-related white matter abnormalities, both diffusely and in multiple focal regions. However, they critically noted that many TBI patients did not have detectable diffusion imaging abnormalities in the white matter, and they concluded that diffusion scans could not be used in place of a clinical, symptom-based diagnosis. Based on findings such as these, Douglas et al. (2015) recently argued that current diffusion techniques are only sensitive to differences at the group level, and have not yet demonstrated clinical utility at the individual level. While diffusion images clearly carry some useful information about TBI, the fact that 1) TBI in some individuals can occur without diffusion abnormalities, and 2) diffusion effects do not explain TBI-related influences on brain communication, suggests that in at least some individuals, TBI-related injuries that affect brain function may occur outside the deep white matter structures that are the primary focus of diffusion scans.
The loss of axonal myelin sheathing may be a major contributing factor to TBI-related disruptions in white matter integrity and resulting symptoms (Shi et al., 2015). Importantly, this myelin sheathing is not present only in the deep white matter. Rather, it extends into the cortical mantle itself, where initial and terminal processes of large pyramidal cells, as well as short-range axonal processes of cortical interneurons, are often heavily myelinated (Nieuwenhuys, 2013). Such intracortical myelination has been the basis of a great deal of study, most notably inspiring the subfield of myeloarchitectonics as an alternative (Hopf, 1951) or complement (e.g., Palomero-Gallagher and Zilles, 2017) to the study of the cytoarchitectonic organization of the brain. TBIs, which are argued to affect cognition and behavior via the damage and demyelination of long-range axons, could also plausibly reduce intracortical myelination. If true, intracortical myelin could serve as a diagnostic biomarker for TBI that, in being localized to cortex, better relates to cortical function, and so is complementary to measures of deep white matter myelination.
Unfortunately, the integrity of these myelinated intracortical axons is difficult to measure using standard diffusion imaging, as the myelinated axons in cortex are not as dense or as consistently aligned as they are in subcortical white matter tracts. While multiple works have demonstrated some ability characterize axonal orientations in cortex using very high-resolution diffusion imaging (Heidemann et al., 2012; McNab et al., 2013; Song et al., 2014) or novel analysis techniques (Rathi et al., 2014), it is unclear to what extent these techniques can actually assess axonal integrity or myelination within the cortex.
Importantly, in 2011, Glasser and Van Essen introduced a noninvasive MRI-based technique to map the density of intracortical myelin in the human brain. This technique emerged from the realization that both T1-weighted (Sigalovsky et al., 2006) and T2-weighted (Yoshiura et al., 2000) MRI images are partially sensitive to cortical myelin content, but in different directions; and thus, that calculating the ratio between the T1-w and T2-w image could dramatically improve myelin-related contrast. This myelin mapping technique, which has been shown to converge with histologically-derived maps of cortical myelin (Nieuwenhuys and Broere, 2017), has illustrated the distribution of intracortical myelin across the cortex in living humans (Ganzetti et al., 2014; Glasser and Van Essen, 2011; Glasser et al., 2016; Shafee et al., 2015), and has been used to parcellate the cortex into cortical areas (Glasser et al., 2016). It has also been used to show that intracortical myelin 1) is affected by aging and associated with cognitive performance (Grydeland et al., 2013); 2) is associated with individual differences in fMRI- (Gordon et al., 2017a) and EEG-derived (Grydeland et al., 2016) measures of cortical function; and 3) is reduced in multiple sclerosis (Righart et al., 2017) and schizophrenia (Iwatani et al., 2015). Together, this work suggests that the myelin mapping technique provides information that is relevant for brain organization and cortical function, and is sensitive to disease states. However, this approach has not previously been explored as a potential biomarker for TBI-related damage.
Here, we explored whether measures of intracortical myelin are sensitive to the number of lifetime TBIs by employing the T1-w/T2-w myelin mapping technique in 46 U.S. military Veterans with a history of TBI. Because TBI-related reductions in axonal integrity have previously been described as occurring both strongly in specific focal regions and diffusely across the brain (Davenport et al., 2012; Jorge et al., 2012; Mac Donald et al., 2011), we tested for effects of TBI on cortical myelin both in specific focal cortical regions and diffusely across the cortex. Further, because exposure to a blast has been argued to alter white matter integrity through different mechanisms than blunt-force impact (Bass et al., 2012; Davenport et al., 2012; Ganpule et al., 2013; Taber et al., 2015), we tested whether a history of blast-induced TBI alters cortical myelin content above and beyond the number of TBIs. Finally, we collected resting-state fMRI in a subset of patients in order to test whether TBI-influenced intracortical myelin is associated with the strength of networked brain communication.
Section snippets
Participants
Data were collected from US Military Veterans with a self-reported history of TBI recruited from the areas surrounding Waco, TX. Participants were carefully selected as a subset of 62 individuals from three separate studies who exhibited the highest quality MRI images (see “Quality control of MR images”, below). The final sample included 46 Veterans (40M, 6F).
Before beginning their study participation, participants were screened and excluded for MRI safety issues, any Axis I psychotic disorder,
Participant characteristics
The forty-six participants retained for the final analysis had an average age of 37.4 ± 9.8 years. On average, these participants had experienced 3.2 ± 2.1 lifetime TBIs (range: 1–9). The average amount of time that had passed since the most recent TBI was 9.0 ± 9.6 years (range: 3 months–41 years).
In characterizing the worst lifetime TBI, we found that 35 participants had experienced only mild TBIs; 8 had experienced at least one Moderate TBI; and 3 had experienced at least one Severe TBI. For
Discussion
In this work, we conducted the first examination of how TBI affects intracortical myelin in the human brain. We found that suffering an increasing number of lifetime TBIs broadly and diffusely reduced the strength of the myelin-sensitive contrast within the cortex. We also found a specific region in lateral occipital cortex that exhibited TBI-related reductions in the intracortical myelin contrast, which were associated with lower functional connectivity. Together, these results suggest that
Conclusion
Here we demonstrated for the first time that intracortical myelin, as measured using the T1-w/T2 ratio method of myelin mapping, is sensitive to TBI, and that TBI-related disruption of intracortical myelin affects brain function. This finding suggests that intracortical myelin may be useful as a biomarker of TBI. As intracortical myelin is anatomically complementary to diffusion-based measures of deep white matter, future work should explore whether measures of intracortical myelin may usefully
Funding
This work was supported by Career Development Award #1IK2CX001680 from the US Department of Veterans Affairs Clinical Sciences Research and Development Service, and by VA VISN17 Center of Excellence pilot funding. The contents of this manuscript do not represent the views of the US Department of Veterans Affairs or the United States Government.
Acknowledgments
The authors thank Ramy Sweidan, Brad Gary, Staley Justice, Leslie Danhaus, Habib Abla, Krupa George, Janani Srikanth, Allison McGinnis, and Robert Athey for their assistance in data collection and behavioral data coding.
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