Keywords:
Craniocerebral Trauma - Diffuse Axonal Injury - Diffusion Tensor Imaging - Glasgow
Outcome Scale - Regeneration
Palavras-chave:
Traumatismos Craniocerebrais - Lesão Axonal Difusa - Imagem de Tensor de Difusão -
Escala de Resultado de Glasgow - Regeneração
INTRODUCTION
Traumatic brain injury (TBI) causes different complex brain lesions such as hematomas,
contusions, vascular injuries, and diffuse axonal injury (DAI). DAI results from high-energy
acceleration and deceleration forces, determining shearing strains in the white matter,
leading to disconnection or dysfunction of the neural network[1].
Head injuries, particularly DAI, result in distinct functional deficits, such as physical,
cognitive, and behavioral impairments, which dramatically affect life quality, return
to daily activities, and social reintegration of survivors[2]. In 1975, Jennett and Bond developed the Glasgow Outcome Scale (GOS), and it was
used as a primary outcome measure in phase III trials in TBI[3],[4]. Afterward, acknowledging some limitations of the GOS, the Glasgow Outcome Scale
- Extended (GOS-E) was developed. Since its establishment in 1981, it has been used
and recommended as the primary outcome measurement in TBI studies[5],[6].
DAI is not only restricted to mechanical forces at the moment of the trauma. Many
different processes are triggered, such as inflammatory responses, molecular changes,
apoptosis, and Wallerian degeneration. Therefore, the pathophysiology of DAI can be
divided into primary and secondary lesions. The primary axonal lesion is the complete
disconnection related to the kinetic energy in the moment of trauma. In contrast,
secondary axonal injuries are indirect and progressive lesions in neurons that occur
late after the initial shock[7]. The impact sparks molecular and cellular events that disturb the homeostasis, leading
to changes in neurons and to the regional microglia that can persist for years[8].
Traditional imaging modalities such as computed tomography and standard magnetic resonance
(MR) sequences, such as T1 and T2 weighted sequences, are not sensible enough to show
the white matter (WM) damage related to DAI. Diffusion tensor imaging (DTI) is an
advanced MR modality based on water molecules diffusion that measures the preferential
displacement along the white matter tracts and has been used to assess the brain microstructure
in different pathologies, including head injuries[9]. There are diverse methods available to analyze DTI images, such as region-of-interest
analysis and tractography. One of the most commonly used is the whole-brain approach
for group comparisons, for which tract-based spatial statistics (TBSS) is particularly
recommended for voxel wise and cluster-based analyses, constraining statistical analysis
to the center of the tracts[9]. It is a semi-automated method, with minimal user-dependence, that allows a whole-brain
evaluation and is notably suitable for evaluating diffuse lesions in the brain parenchyma
such as DAI[10],[11].
Other groups have used this approach to assess white matter changes in victims of
head injury in different stages after trauma[12],[13]. Lipton and colleagues conducted a study on patients with mild TBI who presented
with persistent cognitive impairment eight months to three years after the trauma.
They found decreased fractional anisotropy (FA) and increased mean diffusivity (MD)
in the corpus callosum, subcortical white matter, and internal capsules compared to
healthy controls[13]. Another group investigated adolescents with mild TBI in the acute phase (from 1
to 6 days after the trauma event) compared to age-matched controls[14]. They found significantly decreased apparent diffusion coefficient (ADC) and radial
diffusivity (RD) and increased FA in several white matter regions and the left thalamus,
consistent with axonal cytotoxic edema in the acute phase post-injury. However, few
published works analyzed the progressive changes in the white matter in DAI, particularly
in moderate and severe trauma victims.
This study aimed to investigate longitudinally the white matter of patients with severe
and moderate DAI at two moments defined as the subacute (two months) and early chronic
phases (one year) following the trauma event. We also assessed patients’ clinical
outcome one year after trauma using the GOS-E scale[6]. Our central hypothesis is that DTI parameters change with time and can have a degree
of correlation with functional outcome.
METHODS
Standard protocol approvals
The protocol was reviewed and approved by the institutional review board, the local
ethics committee, and all participants gave written informed consent.
Study design and subjects
A prospective study was conducted throughout one year. Adult outpatients admitted
at the Emergency Room of Hospital das Clínicas, Faculdade de Medicina da Universidade
de São Paulo, Brazil, victims of moderate and severe TBI (Glasgow Coma Scale scores
between 3 and 12 at initial evaluation), presenting clinical and tomographic findings
exclusively of DAI were eligible to be included in the study. Exclusion criteria were
the presence of contusions greater than 10 cm3, midline shift greater than 0.5 cm, extra-axial collection determining compression
of the brain parenchyma, or any indication for surgical intervention. Patients with
poor quality imaging studies that limited analysis, clinical contra-indications that
precluded MR scanning, or loss of follow-up were also excluded.
Data acquisition
All data were acquired on a 3T system (Intera Achieva, Philips Healthcare, Best, The
Netherlands). Patients were scanned using an 8-channel head proton coil (Philips Healthcare,
Best, The Netherlands) at two time-points: two months (subacute phase) and one year
(early chronic phase) after the trauma. The routine protocol included fluid-attenuated
inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and susceptibility-weighted
imaging (SWI) sequences. For the data analysis in this study, we used a volumetric
T1-weighted and DTI sequences.
The 3D-T1 fast field echo, acquired in the sagittal plane, was obtained using the
following parameters: FOV 240 x 240 x 180 mm3; matrix 240 x 240 mm; isotropic resolution; TR/TE 6.2/2.7 ms; and acquisition time
4.13 min.
The DTI sequence was acquired in the axial plane, using 32 directions and one b0 using
the following parameters: 70 slices; slice thickness 2 mm; no gap; field of view 256
x 256 mm; voxel resolution = 2 mm3 (isotropic); TR/TE 8.500/61 ms; b = 1000 s/mm2; matrix 128 x 128; number of excitations (NEX) = 1; and acquisition time of 7 minutes.
Imaging processing and analysis
Initially, all diffusion images were pre-processed for eddy current corrections and
extraction of non-brain voxels, using FMRIB's Diffusion Toolbox (FSL) software, version
5.0.11[9],[15]. For motion correction, the free toolbox Explore DTI (A. Leemans, University Medical
Center, Utrecht, The Netherlands) was used, which rotates the B-matrix while keeping
the exact initial orientation. With this same software, visual quality inspection
for residuals and outliers was performed in each data set[16],[17].
Thereafter, FA maps were analyzed using TBSS[9]. All individual FA images were non-linearly registered to the most typical subject
of the sample (using -n command), and then the aligned dataset was transformed into
the MNI152 standard space (1 mm[3]). The mean aligned FA images were merged into a single four-dimensional (4D) average
FA image. A mean FA skeleton was extracted from the generalized 4D image, and the
tracts were projected into the skeleton, using a 0.2 threshold[18]. To extract mean, axial, and radial diffusivities (MD, AD, and RD, respectively),
non-linear warps and skeleton projections were applied to each DTI scalar parameter.
Statistical analysis
To assess differences in FA, MD, RD, and AD with time, we performed one-sample t-tests,
using the average difference between the two measures across subjects. Initially,
the difference between the subacute and the early chronic phase was calculated, and
then the early chronic value minus the subacute phase value was calculated. Permutation-based
nonparametric inferences were made on unsmoothed statistical maps, using 5000 permutations,
and the cluster-like structures were enhanced using the threshold-free cluster enhancement
(TFCE) algorithm[19]. This approach was similarly applied to the MD, AD, and RD maps. Data were corrected
for multiple comparisons, using the family-wise error (FWE) rate, setting the significance
level at p < 0.05.
Thenceforth, the cluster tool (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Cluster) was applied to extract the exact clusters, followed by the Atlasquery tool to obtain
the coordinates (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlasquery) according to the Johns Hopkins University (JHU) white matter tractography atlas.
Outcome measure
We used the GOS-E at 12 months post-injury obtained at the medical appointment follow-up,
which has been recommended as the main outcome measurement in TBI studies[7]. It consists in an eight-scale global measure of function, used to estimate physical
disability grading[6]. It classifies patients into upper and lower levels of good recovery (GOS-E = 7
and 8), moderate disability (GOS-E = 5 and 6), severe disability (GOS-E = 3 and 4),
vegetative state (GOS-E =2) and death (GOS-E =1).
Association analysis
The WM areas with FA differences with time were defined as ROIs and the mean FA values
of each one was calculated. Then, to test for association of mean FA values of each
ROI with GOS-E grading, we used Cohen’s d effect size test. We segmented patients
into two different groups: sub-optimal (GOS-E= 5 or 6) and optimal (GOS-E = 7 or 8)
performance. We tested for associations of each ROI at two months and one year after
trauma.
Taking into account the relatively small patient sample, we also estimated Cohen’s
d effect size test considering a bigger sample size (4 times our sample, with the
same distribution).
RESULTS
In the initial screening, 225 patients with head trauma were evaluated, and the final
analysis included twenty of those patients. Demographics of the final sample are described
in [Table 1]. Two hundred and five subjects were excluded for the following reasons:
-
- 186 had no clinical and/or tomographic criteria for DAI;
-
- 7 follow-up losses;
-
- 5 were not eligible for MRI;
-
- 5 had low-quality DTI studies;
-
- 1 developed epidural compressive hematoma;
-
- 1 died.
Table 1
Demographics of the 20 patients included in the study.
|
Demographics
|
|
Sex
|
Male = 16
|
|
Female = 4
|
|
Handedness
|
Right-handed = 16
|
|
Left-handed = 4
|
|
Traumatic event
|
Motorcycle = 10
|
|
Car accident = 6
|
|
Run over = 3
|
|
Agression = 1
|
|
GCS at hospital admission
|
Moderate (GCS 9-12) = 14
|
|
Severe (GCS < 8) = 6
|
|
Interval between trauma and hospital admission
|
39 minutes (15 to 77 minutes)
|
|
One-year outcome
|
GOS-E 5 = 1
|
|
GOS-E 6 = 7
|
|
GOS-E 7 = 11
|
|
GOS-E 8 = 1
|
GCS: Glasgow coma scale; GOS-E: Glasgow outcome scale extended.
Evaluation of changes between two months and one year after trauma (chronic minus
subacute volumes) with voxel-based TFCE analysis indicated brain regions with FA increment
with time, predominantly in the right hemisphere and in the left cerebellum. Significant
brain clusters ([Table 2]) were found in the right superior longitudinal fascicle, the temporal part of the
right superior longitudinal fascicle, right inferior fronto-occipital fascicle, right
superior and inferior longitudinal fascicles, the body of corpus callosum, forceps
major and left corticospinal tract ([Figure 1]). Moreover, we found extensive areas of increases in MD, RD, and AD (p < 0.05, FWE
corrected) ([Figure 2]).
Table 2
Significant clusters found in FA analysis.
|
Cluster Index
|
Voxels
|
p
|
X (mm)
|
Y (mm)
|
Z (mm)
|
Location
|
|
7
|
7
|
0.007
|
25
|
-44
|
36
|
R SLF, R IFOF
|
|
6
|
4
|
0.025
|
3
|
3
|
26
|
body corpus callosum
|
|
5
|
4
|
0.024
|
32
|
-41
|
30
|
R SLF, R SLF (temporal part), R ILF
|
|
4
|
2
|
0.027
|
-28
|
-47
|
-38
|
L CST
|
|
3
|
1
|
0.04
|
29
|
-68
|
11
|
forceps major, R IFOF, R ILF
|
|
2
|
1
|
0.038
|
30
|
-43
|
32
|
R SLF and R ILF
|
|
1
|
1
|
0.031
|
33
|
-35
|
32
|
R SLF, R SLF (temporal part)
|
R SLF: right superior longitudinal fascicle; R IFOF: right inferior frontal-occipital
fascicle; R ILF: right inferior longitudinal fascicle; L CST: left cortical spinal
tract .
Figure 1 The most significant clusters found with increments in FA (early chronic phase minus
subacute phase) are shown in red, TFCE (p < 0.05, FWE corrected). The mean FA skeleton
is indicated in white.FA: fractional anisotropy.
Figure 2 White matter differences between early chronic and subacute phases. Significant clusters
(p < 0.05, FWE corrected). Blue depicts MD, yellow AD, and green RD increases in the
chronic phase.FWE: family-wise error; MD: mean diffusivity; AD: axial diffusivity;
RD: radial diffusivity.
Of note, the one-sample t-test used to assess the difference between subacute and
early chronic volumes did not demonstrate significant differences for any DTI parameter.
Correlations between the different FA ROIs and the one-year GOS-E grades were tested
with different ROIs at 2 months and 1 year post-trauma ([Figures 3] and [4]). We did not find any correlations on either moment.
Figure 3 Subgroup analysis. The forest plot shows the effect size in the outcome variable
across the pre-specified subgroups according to GOS-E outcome stratification (moderate
disability vs good recovery). Association analysis between different ROIs at 2 months after trauma
with sub-optimal and optimal 1-year post-trauma GOS-E scores. Horizontal axis indicates
differences between the groups of recovery according to each cluster. Effect size
values are displayed with respective 95% confidence intervals and statistical significance
(p) obtained by Cohen’s d test (squares).
Figure 4 Subgroup analysis. The forest plot shows the effect size in the outcome variable
across the pre-specified subgroups according to GOS-E outcome stratification (moderate
disability vs good recovery). Association analysis between different ROIs at one year after trauma
with sub-optimal and optimal 1-year post-trauma GOS-E scores. Horizontal axis indicates
differences between groups of recovery according to each cluster. Effect size values
are shown with respective 95% confidence intervals and statical significance (p) obtained by Cohen’s d test.
In addition, by hypothetically increasing our sample 4-fold, we found some associations
between one-year GOS-E and the specific ROIs of FA increase at 2 months and 1 year
after trauma ([Table 3]).
Table 3
Correlation analysis artificially increasing the sample size 4-fold.
|
Cluster index
|
p value (2 months)
|
p value (1 year)
|
|
1
|
0.014
|
0.009
|
|
2
|
0.056
|
0.078
|
|
3
|
0.823
|
0.994
|
|
4
|
0.190
|
0.398
|
|
5
|
0.020
|
0.061
|
|
6
|
0.161
|
0.024
|
|
7
|
0.044
|
0.685
|
p value obtained by Cohen’s d test.
DISCUSSION
In our investigation, we performed whole-brain analysis using a semi-automated method
to explore white matter changes over time in moderate and severe TBI victims. DTI
has mainly been used to study white matter in the trauma scenario. However, most published
articles are related to mild trauma and with different follow-up periods[12],[13],[20]. It is important to emphasize that our patient sample is very homogeneous, consisting
of victims with moderate and severe trauma, who were explicitly and exclusively diagnosed
with DAI, and followed for one year after the event.
We found some scattered areas of FA increase, notably in the right brain hemisphere,
accompanied by vast regions in the brain and the cerebellum demonstrating an increase
in MD, RD, and AD over time. Interestingly, patients showed relatively good clinical
outcomes, according to the GOS-E scale. We also found different associations between
each brain region with increased FA and the late clinical outcome (GOS-E) two months
and one year after trauma, which were more prominent when tested in a larger sample
size. Our results are aligned with previous studies that have described white matter
changes on DTI parameters over with time in victims of head trauma[21],[22]. These ongoing DTI parameters are related to different pathophysiological processes
such as inflammation, degeneration, and regeneration - which have already been described
in experimental studies[23],[24].
We identified a general area of increase in MD, AD, and RD in brain tracts one year
after trauma. We consider that the MD increase is mainly a result of high RD values
and, in a lower degree, to AD increment. MD represents the overall diffusivity of
water molecules, which can be related to the increasing content of isotropic tissue
with water content (gliosis)[25]. Although the biological basis for anisotropy and diffusivity changes in tissues
revealed by DTI data is still largely debated, studies using animal models have demonstrated
that axonal injury itself is represented by AD changes, and demyelination is associated
with an increase in RD values[26]. Considering that increases in both AD and RD contribute positively to increase
in MD values, it is reasonable to assume MD as a more sensitive parameter when compared
to FA in our observation.
Moreover, in addition to axonal injury, other important and specific pathophysiological
processes are also present in the trauma scenario, such as neuroinflammation, afferent
degeneration, and debris clearance, and the magnitude of each one at different stages
may imply distinct changes in DTI scalar values. Animal model studies play an essential
role in characterizing these other effects of the trauma event and how they change
over time. However, most of the articles published to date describe the changes that
occur in the early acute time after trauma, and only a limited number of articles
evaluate long-term consequences[27]. It is already well established that the overall axonal injury in trauma survivors
is a consequence of the secondary axonal injury, which is the indirect damage to neurons
related to neuroinflammation and microglial activation, triggered by the initial impact
and that can persist for years[23]. These processes are responsible for biochemical changes leading to local edema
and changes in the microvascular circulation, leading to ischemia and demyelination,
which can be confirmed by the RD increase over time[28]. Moreover, AD increase has been associated with an increase of the extracellular
water content, such as debris clearance, that would ease the water molecule movement
in an axis parallel to the axons[29]. Thereby, we suppose that our results can be explained by the Wallerian or Wallerian-like
degeneration process due to DAI or related to a secondary pathological process, such
as regional ischemia, and neuronal death may ultimately lead to brain atrophy[29].
We also found some spotted areas of FA increase in the right brain hemisphere and
the left cerebellum over time. Different causes can be associated with FA increase,
such as local fibrosis, hemorrhage areas, and neuronal sprouting[30]. FA is related to the microstructural organization, with high values (close to one)
related to most anisotropic tissues. Microstructural organization after trauma has
been reported to start in the first few days and can persist for years, which is linked
to neuroplasticity[31]. The functional recovery accompanied by the increase in FA may somehow be related
to neuroplasticity. Interestingly, we found areas of FA increase in the right brain
hemisphere and in the left cerebellum, which may indicate the involvement of the contralateral
cerebellar hemisphere in functional and compensatory changes after trauma, as it has
been already reported[32]. An interesting functional study compared children with moderate and severe trauma
to controls, showing that children with TBI demonstrated changes in functional cerebral
activity and increased recruitment of neural resources such as the cerebellum[32].
We tested for correlations between mean FA values at the subacute and early chronic
phases of the specific regions that presented significant changes over time and the
GOS-E scores. We could not find any significant correlations, but the lack of significance
may be related to our sample size, which was relatively small when considering the
optimal number of individuals required for correlational studies[33]. Still, some specific regions, such as the right SLF and the body of the corpus
callosum, demonstrated promising effect sizes in functional stratification at the
early chronic phase between optimal and sub-optimal GOS-E scores and mean FA values
by using a theoretical larger sample size.
Whole-brain voxel-wise analysis has been increasingly used to study DAI because of
the widespread nature of the disorder and the advantage of this method being minimally
invasive for multi-subject group evaluation. However, with this technique, it is imperative
the use rigorous statistical procedures to correct for multiple comparison errors,
which reduce the sensitivity for detecting subtle changes[12].
One limitation of our study is the relatively small sample size. However, we included
an homogeneous group of patients with a minimum one-year survival after the traumatic
event, especially considering that victims of moderate and severe head trauma have
high mortality rates in the first six months[34],[35]. Moreover, these patients also presented an excellent recovery with high one-year
GOS-E scores. This may be related to the exclusion of other conditions commonly associated
with a head injury, such as contusions and hematomas that are related to a worse outcome[2].
Concerning the methodology and image acquisition, we must emphasize that more gradient
encoding directions and more robust DTI acquisition and analytical methods such as
high angular resolution diffusion imaging (HARDI), diffusion kurtosis imaging (DKI),
and q-ball imaging are available and could have enhanced the power of data analysis[11],[36]. However, these approaches require longer acquisition times, more sophisticated
algorithms, and are still not feasible to implement in clinical and research scenarios.
In conclusion, our work indicated changes in all DTI scalar metrics in the brain and
cerebellum white matter in a homogeneous group of DAI victims along the first year
following moderate and severe head trauma. This study can be important to guide future
research in understanding the different pathophysiological processes that occur at
different stages of patient recovery. Further studies are expected to show that DTI
is a tool for signaling functional outcomes and is a promising method to guide therapies
and rehabilitation procedures in trauma survivors.
