Abstract

Key words:: Dartmouth Head Injury Model, fiber strain, neural pathway, tractography, traumatic brain injury, white matter injury

Introduction

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality in the United States.¹ To better understand the mechanisms of brain injury at the macroscale level, numerous kinematic injury metrics have been proposed (e.g., peak linear [] and/or rotational [] accelerations, or their derivatives such as Head Injury Criterion [HIC],² Rotational Injury Criterion [RIC] and Power Rotational Head Injury Criterion [PRHIC],³ Brain Injury Criteria [BrIC],⁴ Generalized Acceleration Model for Brain Injury Threshold [GAMBIT],⁵ Head Impact Poser [HIP],⁶ and the HIT severity index⁷). Unfortunately, they are not directly related to the brain mechanical responses believed to initiate the injury,⁸ and as of yet, no consensus has been reached on an appropriate injury metric or a threshold to date. Further, these metrics have been shown to be less effective in injury prediction than brain mechanical responses estimated from validated finite element (FE) models.^3,9–11

Most of the model-based injury studies use isotropic response variables, such as the maximum principal strain (), that are not capable of characterizing white matter (WM) axonal shearing^12,13 or elongation,^14,15 both of which have been thought to cause morphological injury and functional impairment in diffuse axonal injury (DAI) at the microscale level. To address this inconsistency, recent studies have begun to use strains along WM fibers to characterize elongation of axonal bundles (termed “axonal” or “fiber” strain, ).^11,16–22 Initial evidence suggests that fiber orientation-dependent improves injury prediction relative to its isotropic counterpart, ^11,18,21,22

Ji and colleagues²² used subject-specific head FE models to evaluate and compare with for a group of 11 athletes with concussion.²² They found that the two strain measures differed significantly in distribution and extent of WM exposure to high strains. Further, the distribution of WM regions with high was consistent with typical heterogeneous patterns of WM disruptions in DAI. On a group-basis, the average extent of WM injury assessed at an optimal strain threshold also matched well with the percentage of WM voxels experiencing significant longitudinal changes in fractional anisotropy (FA) and mean diffusivity found from another independent imaging study.²³ These findings were supported by the work from Giordano and Kleiven,¹¹ who studied 58 National Football League sports accidents and concluded that performed better in injury prediction than , CSDM (Cumulative Strain Damage Measure based on ²⁴), BrIC, or HIC.

While these studies suggest the importance of WM structural anisotropy,²⁵ continued development along this line of research by incorporating whole-brain tractography derived from diffusion tensor imaging (DTI) for evaluation may improve the injury prediction performance even further. The reason is that tractography potentially allows more accurate estimation of fiber orientations at each individual high resolution sampling point (e.g., every 1mm along fibers or “streamlines”) than those “averaged” at coarse FE elements or voxels (e.g., element size up to 7.73mm,²⁶ or DTI voxel size of 2mm ×2mm ×3.6mm²⁷).

For example, Chatelin and associates¹⁸ used weighted directions averaged from 70–4096 DTI voxels for each element. Both Cloots and coworkers¹⁷ and Wright and Ramesh¹⁹ employed a multiscale approach to couple a macroscale three dimensional (3D) or two dimensional (2D) head model, respectively, with a microscale element embedding a single axon. Work from Wright and colleagues,²¹ Kraft and associates,²⁰ Sahoo and coworkers,²⁶, and Giordano and Kleiven²⁷ assigned average FA values and fiber directions to each element from coregistered DTI for strain evaluation. Instead of averaging on elements, Ji and associates²² evaluated fiber strain at each DTI voxel transformed into the corresponding FE model space using strain tensors from the closest FE element on a subject-specific basis.

Assessing tractography-based fiber strains is of particular interest in WM regions with crossing fibers or other complex fiber configurations. In addition, the ability to evaluate at a finer spatial resolution also enables assessing WM voxel-wise injury vulnerabilities at a scale finer than a simple injured versus noninjured binary status, as is commonly used. Perhaps more importantly, analyzing strains along the entire lengths of fibers enables assessing the injury risks to WM neural pathways or tracts, which is not possible with previous element/voxel-based studies that are limited to anatomical regions of interest (ROIs). Because axons and their bundles are neural processes that connect neuronal cell bodies and transmit information between them,^28,29 it is reasonable to assume that any damage along the axons, regardless of the location (within or outside the boundary of any traversing ROI), would constitute potential functional impairment.²⁰

The goal of this study was to develop a technique to evaluate along voxel- or anatomically constrained whole-brain tractography, which has not been performed before. An “injury susceptibility index” was developed to quantify injury vulnerabilities with a finer, graded “likelihood” of injury of discrete WM voxels or individual neural pathways, extending previous efforts where typically a dichotomous injury status was instead assigned. As an initial step, here we focus our efforts on comparing the new tractography-based method and our previous voxel-based technique for fiber strain analysis using idealized head rotations.

As an illustration, we investigated the injury susceptibilities of the whole-brain WM voxels as well as transcallosal fiber tracts traversing the corpus callosum (CC). Finally, these efforts allowed us to investigate the response's directional sensitivity. If is indeed a more effective injury predictor, our investigation using a realistic 3D model incorporating whole-brain tractography is a significant advancement over previous studies using 2D head models coupled with projections of fiber directions ²¹ or those based on ^30–32 Collectively, these efforts extend our previous investigations^22,33,34 and set the stage for future studies to explore the causal relationships among brain mechanical responses, WM structural integrity, and brain functional alteration or clinical symptomatic measures in subjects with TBI.

Methods

The Dartmouth Head Injury Model

All brain strain responses were obtained using the Dartmouth Head Injury Model (DHIM; Fig. 1). The DHIM is a subject-specific model created with high mesh quality and geometrical accuracy based on high-resolution magnetic resonance imaging (MRI) of an athlete. Details of the model description, material properties, and validation performances were reported previously.^22,35,36

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FIG. 1.

The Dartmouth Head Injury Model (DHIM) showing the head exterior (a) and the brain components (b). The model incorporates whole-brain tractography from the same individual used for the baseline DHIM creation. A subset of fractional anisotropy-encoded transcallosal fibers is shown (b). CSF, cerebrospinal fluid. Color image is available online at www.liebertpub.com/neu

Briefly, the DHIM is composed of solid hexahedral and surface quadrilateral elements with a total of 101.4 k nodes and 115.2 k elements and a combined mass of 4.562kg for the whole head. The brain has a total of 56.6 k nodes and 55.1 k elements, with a combined mass of 1.558kg. The average element sizes for the whole head and the brain are 3.2±0.94mm and 3.3±0.79mm, respectively.

The entire brain is modeled with the C3D8R type of hexahedral elements³⁷ using a homogenous, second-order Ogden hyperelastic material model with rate effects incorporated through linear viscoelasticity. A layer of soft elements representing the cerebrospinal fluid between the brain and its neighboring structures (skull, falx, and tentorium) is incorporated by node sharing to enable brain interfacial sliding.

The DHIM has successfully passed the numerical convergence test and is of high mesh quality in terms of warpage, aspect ratio, skew, Jacobian, minimum length, and minimum/maximum angle.²² In addition, the model has been successfully validated against relative brain-skull displacement^38,39 and intracranial pressure responses^40,41 from cadaveric experiments, as well as full-field strain responses in a live human volunteer.⁴² The overall “good” to “excellent” validation at the low (~250–300rad/s² for the volunteer), mid (~1.9–2.3 krad/s² for impact tests C755-T2 and C383-T1), and high (~11.9 krad/s² for test C393-T4) levels of peak magnitudes as well as pressure responses provided important confidence of the accuracy of DHIM-estimated brain responses. In this study, the high-resolution T1-weighted MRI and DTI of the same individual used to develop the baseline DHIM were used for image registration, tractography, and strain computation.

Voxel- and anatomically constrained fiber tract clustering

Intravoxel fibers traversing a given voxel were identified from the whole-brain tractography by testing whether any fiber sampling point fell inside the boundary of the corresponding hexahedral element converted from the voxel. Automatic clustering of fiber tracts/bundles based on anatomical ROIs is still an active, ongoing research topic. Here, we adopted an existing method⁴⁴ to identify transcallosal fibers using the ICBM DTI-81 WM atlas⁴⁵ as an anatomical constraint. This atlas is a stereotaxic probabilistic map averaged from 81 normal subjects with hand-segmented WM parcellation of 50 deep WM structures. The atlas is provided within a standard anatomical template (ICBM-152, isotropic image resolution of 1mm⁴⁵), which allows for transformation of a given individual's MRI and WM atlas into the same coordinates via affine registration. The registration was performed between the MPRAGE MRI of the individual selected for DHIM creation and the ICBM-152 template, using the ITK Segmentation and Registration Toolkit (Version 4.7; Fig. 2).

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FIG. 2.

Checkerboard images overlaying the individual's magnetic resonance image (appears lighter) with the coregistered ICBM-152 template (background; appears darker) on two axial planes (a,b). The alignment (arrows) suggests a satisfactory registration. A 10% randomly selected subset of the individual's fractional anisotropy-encoded, whole-brain tractography is shown in the ICBM-152 space (c). Color image is available online at www.liebertpub.com/neu

Transcallosal fibers from the whole-brain tractography were identified by testing whether they traversed the genu, splenium, or main body of the CC based on the ICBM DTI-81 WM atlas. This could be determined by first transforming the fiber sampling points into the WM atlas space and then rounding their coordinates to their closest voxels to perform a voxel-wise overlapping test.⁴⁴ Alternatively, we converted CC voxels into hexahedral elements instead and directly tested whether real-valued coordinates of fiber sampling points fell inside the outer boundary of the aggregated hexahedral mesh. This avoided rounding errors and reduced the number of spuriously intersecting fibers.

To further minimize “outlier” fibers spuriously intersecting the target region, thresholding was performed based on the number of intersection points.⁴⁴ Additional filtering based on geometry and tract self-consistency was also performed (e.g., by fitting each fiber into a plane to test its orientation, or by assessing the average distance from fiber sampling point relative to the tract centroid). For each CC subregion, less than 3% of the total number of fibers were considered spurious and were thus removed. This led to 1740, 2866, and 2661 fibers retained for the genu, splenium, and main body of the CC, respectively. The clustering process is illustrated in Figure 3.

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FIG. 3.

Illustration of anatomically or region of interest (ROI) constrained fiber tract clustering. (a) Fractional anisotropy-encoded whole-brain tractography overlaid within the Dartmouth Head Injury Model brain mesh boundary; (b) anatomical boundaries of the three corpus callosum subregions; (c) the resulting ROI-constrained transcallosal fiber tracts; (d) spurious fibers further filtered. A 10% randomly selected fiber sample is shown. Color image is available online at www.liebertpub.com/neu

Idealized head impacts

Brain responses corresponding to axial, coronal, and sagittal (anterior-to-posterior) rotations (Fig. 4a) were selected from an existing pre-computed brain response atlas (pcBRA^32,46). The pcBRA employs triangulated impulses applied to the head center of gravity (skull, scalp, and facial components were simplified as rigid bodies). The shape of the acceleration impulse is not important (e.g., triangular, sine, or haversine), as long as the peak rotational velocity and impulse duration remain identical.⁴⁶

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FIG. 4.

Illustration of the three idealized head rotations (a) and the acceleration/velocity profile used for impact simulation (b).

For the selected brain responses, the impulse had a peak magnitude of 4.5 krad/s², which was the 95th percentile of all measured peak magnitudes observed in collegiate ice-hockey (concussed and nonconcussed⁴⁷) and was slightly below the median or average peak magnitudes for concussive impacts measured in collegiate football.⁴⁸ The impulse duration was 10 msec (average value for found in high-school football⁴⁹; report on typical duration for unavailable). The total simulation time was 28 msec with a temporal resolution of 1 msec, which was composed of a 10 msec acceleration impulse plus an additional 18ms “hold” with zero (i.e., at a constant rotational velocity) to ensure peak responses were captured (Fig. 4b).

The choice of this particular acceleration-only profile without deceleration was made to mimic actual on-field head impact data available, where a single largest acceleration peak dominates.^50,51 Linear acceleration, , was not used here because -induced brain strain responses are negligible because of the near incompressibility of the brain. This was confirmed in terms of both maximum principal strain, (using DHIM and an independently developed head model, SIMon³⁶), and WM fiber strain, ²¹

Injury susceptibility index

For each WM DTI voxel, the accumulated peak , regardless of the time of occurrence, , was computed for all its enclosing sampling points based on the Lagrangian strain tensor.²² Each sampling point was considered “injured” when its exceeded a given threshold, . A voxel-wise injury susceptibility index, , was defined as the fraction of “injured” sampling points within the voxel, or formally:

The value of in percentage ranges from 0% (no injury) to 100% (fully damaged).

Quantifying WM structural integrity or the “likelihood” of injury locally at each voxel is important to correlate brain mechanical responses with neuroimaging at the voxel level. Nonetheless, it does not capture the degree of functional impairment of a neural pathway, which could be of interest for clinical symptomatic measures. Here, we considered an anatomically constrained fiber (i.e., a single streamline in tractography passing through a given ROI) as injured when at least one sampling point, regardless of the location — either within or outside the ROI boundary—has exceeding . This is important, because global functional impairment of a fiber could occur regardless of the injury location. A tract-wise injury susceptibility index () was therefore analogously defined as the fraction of injured fibers from the entire tract:

The injury susceptibility indices depend on a pre-defined injury threshold. For illustration purposes, here we selected two representative values (0.18 and 0.09, corresponding to the upper and lower bounds of a conservative threshold; the former is also an “optimal” threshold¹⁴) to create injury susceptibility maps based on the voxel-wise average . While these injury thresholds drawn from an in vivo animal study do not apply directly to the human brain, the optimal value was comparable to thresholds established from other real-world injury analyses in human TBI studies (albeit, based on ; e.g., 0.21 in the CC or 0.26 in the gray matter, or 0.19 in the gray matter^52,53). The lower bound value was also selected here because it was likely relevant to subconcussive impacts to the brain (which most likely correspond to a lower threshold).

Strategies to assess tract-wise injury susceptibility

Effectively, incorporates the axonal structural information to evaluate injury susceptibilities of WM neural tracts. Current model-based studies, however, typically assess the degree of injury based on an aggregated volume fraction of the whole-brain or specific anatomical ROIs exceeding a threshold, regardless of the location or distribution of high strain exposures.^3,4,31,54 Therefore, we further compared with injury susceptibility measures based on the number or “fraction” of all sampling points as well as all traversing WM voxels for a given tract, regardless of whether injury occurred along the same or different fibers. Two additional injury susceptibility measures are defined, analogously to the CSDM²⁴ but applied to a neural tract:

and

The different WM injury susceptibility indices are illustrated in Figure 5.

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FIG. 5.

Illustration the different injury susceptibility indices (see text for details). ROI, region of interest.

Results

The voxel- and tractography-based approaches produced largely similar distribution patterns for real-valued for the three idealized impacts (Pearson correlation coefficient of 0.92, 0.91, and 0.90 for the axial, coronal, and sagittal rotation, respectively, with p=0; Fig. 6). Not surprisingly, the latter produced smoother distributions because more sampling points were used to compute an average for each voxel (vs. effectively only one with the former). The corresponding maps of (0.09) are also shown.

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FIG. 6.

Maps of using the voxel- (top) and tractography-based (middle row) approaches, along with the injury susceptibility maps (bottom) on a selected axial or sagittal magnetic resonance image in axial, coronal, and sagittal rotation (from left to right, respectively). Color image is available online at www.liebertpub.com/neu

The tractography-based binary or dichotomous injury maps (thresholded from real-valued maps in Fig. 6) were compared against those generated from the voxel-based approach (Fig. 7). The two evaluation techniques produced discordant distributions of voxels susceptible to injury, especially when was high (the average Dice coefficient, d, dropped from 0.81 to 0.66 when increased from 0.09 to 0.18; Table 1). For all injured voxels predicted by at least one approach, one-tailed two-sample t tests indicated that voxels consistently predicted as injured by both techniques had a significantly higher mean FA value than those predicted by only one method, regardless of or rotational direction (p<0.01; Table 1). Interestingly, both approaches predicted a comparable extent of potential injury for the whole-brain WM voxels, or proportion of CC subregions affected, regardless of or rotational direction (Table 2).

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FIG. 7.

The ICBM-81 white matter (WM) atlas warped onto the individual's anatomical magnetic resonance image (MRI) (a and e; on axial and sagittal MRI, respectively). Binary injury maps overlaid on an axial (for axial (b and f) and coronal (c and g) rotations) and a sagittal (for sagittal rotation; d and h) MRI. “Injured” voxels (i.e., corresponding to > ) are color-coded depending on whether they were predicted by both or by only one of the strain evaluation techniques. For each MRI shown, the corresponding Dice coefficient, d, is reported. ( and , refer to obtained from the tract- and voxel-based techniques, respectively). Color image is available online at www.liebertpub.com/neu

Table 1.

Comparison of Predicted Distributions of Potential “Injury” Locations in Terms of Dice Coefficient, d, between the Voxel- and Tractography-Based Approaches at Two Levels

			Axial	Coronal	Sagittal	Avg.
0.09		Dice coefficient, d	0.86	0.81	0.76	0.81
	FA	Both predicted “injury”	0.51±0.16 (8.6 k)	0.51±0.18 (3.0 k)	0.48±0.15 (3.2 k)	0.50
		Only one predicted “injury”	0.34±0.16 (2.7 k)	0.40±0.16 (1.4 k)	0.41±0.15 (2.0 k)	0.38
0.18		Dice coefficient, d	0.74	0.62	0.63	0.66
	FA	Both predicted “injury”	0.51±0.16 (1.7 k)	0.44±0.22 (126)	0.40±0.13 (83)	0.45
		Only one predicted “injury”	0.42±0.16 (1.2 k)	0.37±0.16 (152)	0.33±0.15 (96)	0.37

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FA, fractional anisotropy.

Voxels consistently predicted as “injured” by the two approaches had a mean FA value significantly higher than those predicted by one but not the other. The numbers of identified voxels for each case are shown in parentheses.

Table 2.

Summary of the Predicted Extent of Injury (as Percentages) for the Whole-Brain White Matter Voxels and the Three Corpus Callosum Subregions Using the Voxel- and Tractography-Based (in Parenthesis) Approaches at Two Levels

		Axial %	Coronal %	Sagittal %
	Whole-brain	14.84 (16.05)	5.71 (5.99)	6.19 (6.61)
	Genu	35.53 (37.06)	1.53 (2.01)	2.20 (1.81)
0.09	Main body	15.61 (16.85)	14.48 (15.31)	0.95 (0.59)
	Splenium	27.04 (28.72)	7.23 (7.48)	3.18 (2.55)
	Whole-brain	2.95 (4.12)	0.29 (0.34)	0.15 (0.25)
	Genu	20.06 (23.30)	0 (0)	0 (0)
0.18	Main body	3.56 (4.57)	0.83 (1.31)	0 (0)
	sSlenium	0.12 (0.37)	0 (0)	0 (0)

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To further compare the two strain evaluation approaches, three representative voxels in the axial rotation were selected that were either consistently predicted as potentially injured by both techniques or by one but not the other (Fig. 8). Their response histories are shown (Fig. 8b). When intravoxel fibers were highly aligned (corresponding to a high FA value for the voxel; Fig. 8c), responses varied smoothly across the sampling points. Heterogeneity and discontinuity were evident when otherwise (Fig. 8d and e).

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FIG. 8.

Illustration of responses of intravoxel sampling points in three voxels. Intravoxel fiber sampling points are color-coded by from the tractography-based approach () using strain tensors from their nearest elements, as shown. n, number of intravoxel fibers, which was 45±35 on average (range 0–269) for whole-brain white matter voxels; , angle between each fiber and the voxel-based average fiber orientation (arrows in b–d). All results based on of 0.09. (a) Voxel locations. (b) Time histories of for the three identified voxels. (c) Both the voxel- and tractography-based approaches predicted potential “injury” for voxel #1. (d) The voxel-, but not the tractography-based, method predicted “injury” for voxel #2. (e) Vice versa for voxel #3. CC, corpus callosum. Color image is available online at www.liebertpub.com/neu

For the range of values, was computed for each CC subregion and head impact (Fig. 9). Consistent with the distributions of real-valued (Fig. 6) and thresholded, binary-valued injury locations (Fig. 7), the axial and sagittal rotations appeared the most and least damaging, respectively, in terms of the number of voxels with high exposures. The relative vulnerabilities of the three CC subregions, however, depended on the direction of head rotation (e.g., the genu was the most vulnerable in an axial rotation, but was the least in the other two rotations). The relative vulnerabilities were similarly observed using and that employed the aggregated numbers of injured sampling points and voxels, respectively (Fig. 10). They were significantly lower, however, than the corresponding based on the injured fibers that incorporates WM axonal structural information.

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FIG. 9.

Tract-wise injury susceptibility index, , as a function of for each corpus callosum (CC) subregion in (a) axial, (b) coronal, and (c) sagittal rotations. The results from the same 10% subset were virtually identical to those from the full set. At the “optimal” of 0.18, the largest of 96.8% was observed for the genu in the axial rotation. The largest values of 25.3% and 8.3% occurred in the CC main body with coronal and sagittal rotations, respectively.

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FIG. 10.

Injury susceptibility indices, and , as a function of in (a) axial, (b) coronal, and (c) sagittal rotations. For each corpus callosum subregion, and were largely similar, but were significantly lower than the corresponding (Fig. 9). At the “optimal” , the largest and were 14.6% and 11.5% in the genu in the axial rotation, respectively, and were virtually zero for the other two rotations.

Discussion

Recent brain injury studies suggest the importance of incorporating WM structural anisotropy for improved injury prediction performance.²⁵ Here, we extended previous work that used average orientations on coarse elements^18,20,21,27 or voxels²² to analyze accumulated peak fiber strain, , along the entire lengths of fibers using whole-brain tractography. Each fiber was represented by high resolution (1mm) sampling points, improving on the native DTI voxel resolution (2mm). More importantly, a tractography-based analysis allowed assessment of injury vulnerabilities of entire WM neural pathways, which is not possible with previous ROI-based methods.

Assessing the mechanical responses of functionally important WM neural pathways may be of particular interest for future studies that correlate them with subtle changes in cognition for brains with or even without clinically diagnosed concussion.^33,55 As an initial illustration, we have demonstrated this tractography-based technique using idealized head impacts in the context of sports-related concussion for all WM voxels as well as three transcallosal fiber tracts, with results compared with those from a voxel-based approach.

At the voxel level, a unique capability of our tractography-based technique that appears unachievable with an element/voxel-based analogue was to enable assessment of the “degree” (vs. dichotomy) of WM structural damage. This was quantified in terms of a voxel-wise injury susceptibility index (), which is essentially the proportion of “injured” intravoxel sampling points.

This “graded” susceptibility (vs. a binary injury status) may provide a confidence measure in injury definition. For example, it could serve as a voxel selection criterion when comparing brain mechanical responses with longitudinal neuroimaging alterations (e.g., larger values indicate higher likelihood of injury). Potentially, this concept may be further extended to an anatomical ROI, in which case a can be analogously defined to characterize a regional injury susceptibility (Fig. 5). Such voxel-wise or ROI-based utilities await further exploration.

Interestingly, with an empirical injury susceptibility threshold of 50% to define a binary injury, the tractography-based approach predicted an extent of WM injury (i.e., volume fraction or number of injured voxels) comparable to that from the voxel-based technique for all cases evaluated (Table 2). This finding suggests a certain fundamental concordance between the two approaches. For a “binary” injury prediction based on the volume or number of injured WM voxels for the whole-brain, a DTI voxel-based method appears sufficient and may be preferred because of its relatively modest computation relative to the tractography-based analogue (~12min vs. ~66min in this work).

The two approaches can differ significantly, however, in terms of binary injury distribution after thresholding (despite the similar pattern of real-valued maps; Fig. 6), especially in regions with low FA values or at a high , as evidenced by Dice coefficients (Table 1). These observations suggest further studies using real-world injury cases are required to discern the relative injury predictive power of the two competing strain evaluation techniques, e.g., by correlating injury predictions with voxel-wise neuroimaging findings.^23,56

Regardless, another unique capability of our tractography-based technique is to assess injury susceptibilities for functionally important WM neural pathways, which does not appear feasible with a voxel-based approach. Because fibers serve as communication connections between functionally important cortical areas, injury assessment along their entire lengths (vs. confined within an ROI boundary) is likely critical to capturing the overall vulnerability of a given neural pathway. This is illustrated in Figure 8a (arrow), where potential injury locations with high exposures occurred outside the anatomical boundary of the CC, but in tracts directly linked to it. This finding suggests that previous region-based, element-/voxel-wise injury studies may not be able to capture the overall vulnerability of ROI-associated WM neural pathways, which are important structural substrates to brain function.

The degree of damage to neural tracts can be similarly assessed with a tract-wise injury susceptibility index, which is essentially the proportion of potentially injured fibers, a measure intended to approximate that of “injured” axons.⁵⁷ As an illustration, we computed based on for the three major transcallosal fiber clusters traversing the genu, splenium, and main body of the CC. Results from a 10% subset of fibers were virtually identical to that from the full set for all cases evaluated regardless of (a linear fit of between the two sets led to a Pearson correlation coefficient of 0.99 for the pooled data, not shown; Fig. 9).

The feasibility to down-sample while preserving is important, because fibers from tractography themselves are effectively a representative approximation of the actual whole-brain axons. Further, reduced computational cost is an important practical benefit. Excessive down-sampling should be avoided, however, as insufficient sampling may cause to become unreliable.

The was designed to incorporate WM axonal structural information to assess the damage to neural tracts. Current strain-based injury metrics, however, consider the aggregated measure of the injured volume. Therefore, we compared with two additional injury susceptibility indices based on the total number of neural tract sampling points or traversing voxels, and , respectively. For all the three CC subregions regardless of the rotational direction, the two aggregated susceptibility indices were largely comparable between themselves (Fig. 10). This was not surprising, because they effectively sampled the same spatial domain (albeit, with different sampling resolutions). The two aggregated measures, however, were significantly lower than , suggesting their potential underestimation of damage to neural tracts.

The current definition of does not differentiate the relative susceptibility based on the number of sampling points with high exposure or the magnitude of along each fiber. A proper “injury confidence” weighting factor may be effective to further enhance . Regardless, the injury assessment utility of that incorporates WM axonal structural information has not been employed before. This awaits further exploration.

Conclusion

We have developed a tractography-based approach for fiber strain evaluation using voxel- or anatomically constrained whole-brain tractography. The technique potentially improves previous element-/voxel-based techniques because it evaluates strains along the entire lengths of fibers (as opposed to averages on coarse elements/voxels). In addition, this technique enables quantifying the “degree” of local WM structural integrity and injury susceptibilities of functionally important neural pathways, which are not possible with previous methods. Therefore, the tractography-based approach may be important to enable new brain injury analyses in the future.

Voxel- and tract-wise injury susceptibility indices were also established to assess local WM structural integrity and functional damage in neural pathways. Implications from the tract-wise injury susceptibility measure were consistent with previous neuropathological and DTI studies of TBI across a spectrum of injury severities. Potentially, therefore, our study may lay important foundations for follow-on investigations using real-world head impacts and actual brain injury/noninjury cases to explore the multifaceted mechanisms of DAI.

Future investigations will: (1) analyze real-world impacts to confirm the importance of the tractography-based approach and to verify the clinical significance of the WM injury susceptibility measures; (2) apply the technique to other WM neural pathways; and (3) use realistic head impacts to assess the relative injury susceptibilities of WM neural pathways and identify the most vulnerable ones in real-world injury scenarios.

References