Abstract

This study was designed to (i) evaluate the influence of age on diffusion tensor imaging measures of white matter assessed using tract-based spatial statistics; (ii) determine if mild traumatic brain injury is associated with microstructural changes in white matter, in the acute phase following injury, in a large homogenous sample that was carefully screened for pre-injury medical, psychiatric, or neurological problems; and (iii) examine if injury severity is related to white matter changes. Participants were 75 patients with acute mild traumatic brain injury (age = 37.2 ± 12.0 years, 45 males and 30 females) and 40 controls (age = 40.6 ± 12.2 yrs, 20 males and 20 females). Age effects were analysed by comparing control subgroups aged 31–40, 41–50, and 51–60 years against a group of 18–30-year-old control subjects. Widespread statistically significant areas of abnormal diffusion tensor measures were observed in older groups. Patients and controls were compared using age and gender as covariates and in age- and gender-matched subgroups. Subgroups of patients with more severe injuries were compared to age-and gender-matched controls. No significant differences were detected in patient-control or severity analyses (all P-value > 0.01). In this large, carefully screened sample, acute mild traumatic brain injury was not associated with diffusion tensor imaging abnormalities detectable with tract-based spatial statistics.

Introduction

In mild traumatic brain injury (TBI) research, white matter integrity assessment by diffusion tensor imaging (DTI) has been the centre of attention for the last decade (Hulkower et al., 2013). Many studies have reported white matter changes in patients with a history of mild TBI, recently or remotely, although heterogeneous and inconsistent conclusions have been drawn. This heterogeneity can partly be explained by methodological differences and limitations, such as small sample sizes, failure to control for pre-injury health factors, major differences in time from injury to imaging, diverse patient characteristics, and differing DTI analysis techniques. A brief summary of previous DTI studies that focus on the acute phase of mild TBI are presented in Table 1.

Table 1

A brief summary of previous DTI studies that focus on the acute phase of mild TBI

Reference Time frame* Magnet Patients (gender; age) Control (gender; age) Analysis Findings 
Arfanakis et al., 2002 < 24 h 1.5 T 5 (3 M, 2 F; 35.6 ± 14.8) 10 (5 M, 5 F; 28.9 ± 7.6) ROI Decreased FA 
Bazarian et al., 2007 < 72 h 3 T 6 (4 M, 2 F; mean 21.7, range 18–31 years) 6 (4 M, 2 F; mean 21.7, range 18–31 years) WBA, ROI WBA: Decreased trace 
ROI: Increased FA 
Chu et al., 2010 Range 1–6 days 3 T 10 (4 M, 6 F; 15.7 ± 1.18) 10 (4 M, 6 F; 15.7 ± 1.83) WBA, ROI Increased FA, Decreased RD 
Henry et al., 2011 81.9 ± 46.7 h 3 T 16 (16M; 22.1 ± 1.7) 8 (8 M; 22.8 ± 1.5) WBA Increased FA and AD, Decreased MD 
Lipton et al., 2009 Range 2–14 days 3 T 20 (9 M, 11 F; 33.4 ± 8.3) 20 (9 M, 11 F; 34.2 ± 9.3) WBA Decreased FA, Increased MD 
Mayer et al., 2010 12 ± 5.7 days 3 T 21 (8 M, 13 F; 27.45 ± 7.39) 21 (8 M, 13 F; 26.81 ± 6.68) ROI Increased FA, Decreased RD 
Messé et al., 2012 Range 8–21 days 3 T 53 (35 M, 18 F; 35.5 ± 11.0)) 40 (28M, 12 F; 36.3 ± 12.5) TBSS Decreased FA, Increased MD and RD 
Miles et al., 2008 Range 1–10 days 1.5 T 17 (11 M, 6 F; mean 33.44, range 18–58 years) 29 (15 M, 14 F; mean 35, range 18–61 years) ROI Decreased FA, Increased MD 
Toth et al., 2013 <72 h 3 T 14 (9 M, 5 F; 34.9 ± 18.4) 14 (9 M, 5 F; 35.8 ± 18.5) TBSS, volumetric Decreased FA, Increased MD 
Yallampalli et al., 2013 Range 1–16 days 3 T 11 (5 M, 6 F; 15.09 ± 1.14) 11 (5 M, 6 F; 15.82 ± 1.78) ROI (fornix) Decreased FA, Increased nADC 
Reference Time frame* Magnet Patients (gender; age) Control (gender; age) Analysis Findings 
Arfanakis et al., 2002 < 24 h 1.5 T 5 (3 M, 2 F; 35.6 ± 14.8) 10 (5 M, 5 F; 28.9 ± 7.6) ROI Decreased FA 
Bazarian et al., 2007 < 72 h 3 T 6 (4 M, 2 F; mean 21.7, range 18–31 years) 6 (4 M, 2 F; mean 21.7, range 18–31 years) WBA, ROI WBA: Decreased trace 
ROI: Increased FA 
Chu et al., 2010 Range 1–6 days 3 T 10 (4 M, 6 F; 15.7 ± 1.18) 10 (4 M, 6 F; 15.7 ± 1.83) WBA, ROI Increased FA, Decreased RD 
Henry et al., 2011 81.9 ± 46.7 h 3 T 16 (16M; 22.1 ± 1.7) 8 (8 M; 22.8 ± 1.5) WBA Increased FA and AD, Decreased MD 
Lipton et al., 2009 Range 2–14 days 3 T 20 (9 M, 11 F; 33.4 ± 8.3) 20 (9 M, 11 F; 34.2 ± 9.3) WBA Decreased FA, Increased MD 
Mayer et al., 2010 12 ± 5.7 days 3 T 21 (8 M, 13 F; 27.45 ± 7.39) 21 (8 M, 13 F; 26.81 ± 6.68) ROI Increased FA, Decreased RD 
Messé et al., 2012 Range 8–21 days 3 T 53 (35 M, 18 F; 35.5 ± 11.0)) 40 (28M, 12 F; 36.3 ± 12.5) TBSS Decreased FA, Increased MD and RD 
Miles et al., 2008 Range 1–10 days 1.5 T 17 (11 M, 6 F; mean 33.44, range 18–58 years) 29 (15 M, 14 F; mean 35, range 18–61 years) ROI Decreased FA, Increased MD 
Toth et al., 2013 <72 h 3 T 14 (9 M, 5 F; 34.9 ± 18.4) 14 (9 M, 5 F; 35.8 ± 18.5) TBSS, volumetric Decreased FA, Increased MD 
Yallampalli et al., 2013 Range 1–16 days 3 T 11 (5 M, 6 F; 15.09 ± 1.14) 11 (5 M, 6 F; 15.82 ± 1.78) ROI (fornix) Decreased FA, Increased nADC 

*Time from injury to imaging. Reported as mean time ± SD, maximum time or time range. ADC = apparent diffusion coefficient; F = female; FA = fractional anisotropy; M = male; MD = mean diffusivity (= apparent diffusion coefficient); ROI = region of interest; WBA = whole brain analysis.

The current study had three objectives: (i) to evaluate the influence of age on DTI measures and axonal integrity assessed by tract-based spatial statistics (TBSS); (ii) to determine if mild TBI is associated with microstructural changes in white matter in the acute phase following injury, in a large homogenous sample that was carefully screened for pre-injury medical, psychiatric, or neurological problems; and finally (iii) to examine if mild TBI severity is related to white matter changes. Through rigorous inclusion and exclusion criteria, the goal was to reduce or eliminate numerous confounding variables to study a relatively ‘pure’ sample of civilian patients with acute mild TBIs.

Materials and methods

This work is part of the Tampere Traumatic Head and Brain Injury Study. Subjects were enrolled from the emergency department of the Tampere University Hospital between August 2010 and July 2012; all met mild TBI criteria of the World Health Organization’s Collaborating Centre for Neurotrauma Task Force (Holm et al., 2005). The enrolment protocol included three inclusion criteria and nine exclusion criteria, as described in our previous publication (Luoto et al., 2013), that resulted in a small percentage of patients with head trauma being enrolled (the majority of eligible adults were excluded due to comorbidities). Ethics approval was obtained from the Ethical Committee of Pirkanmaa Hospital District, Finland. All patients and controls provided written informed consent according to the Declaration of Helsinki.

Subjects

Of the 75 patients with mild TBI, 45 were male and 30 were female. The mean age was 37.2 ± 12.0 years. Of the 40 control subjects, 20 were male and 20 were female. The mean age for the control subjects was 40.6 ± 12.2 years. The mean time interval between injury and acute clinical assessment was 48.1 ± 45.4 h. The clinical characteristics of the mild TBI sample are presented in Table 2. For mild TBI severity analyses, mild TBI subgroups were formed based on clinical markers as follows: (i) loss of consciousness > 5 min, n = 7; (ii) post-traumatic amnesia > 3 h, n = 25; (iii) acute traumatic lesion on CT and/or MRI (complicated mild TBI), n = 15; (iv) Glasgow Coma Scale = 14, n = 6; and (v) a group of patients with a combination of any of the previous criteria (definite mild TBI), n = 29.

Table 2

Characteristics of classic clinical mild TBI severity markers

 Presence, n (%)
 
Duration
 
 Yes No Mean Median SD IQR 
Loss of consciousness (min) 28 (37.3) 47 (62.7) 0.9 2.2 0–1.0 
Post-traumatic amnesia (h) 69 (92.0) 6 (8.0) 2.66 1.5 3.4 0.1–4.5 
Retrograde amnesia (h) 17 (22.7) 58 (77.3) 0.4 1.7 
Disorientation 53 (70.7) 22 (29.3)     
Focal neurological deficit 17 (22.7) 58 (77.3)     
GCS, 15 points 69 (92.0) 6 (8.0)     
GCS, 14 points 6 (8.0) 69 (92.0)     
Acute traumatic lesion on CT* 7 (9.3) 68 (90.7)     
Acute traumatic lesion on MRI 15 (20.0) 60 (80.0)     
    Diffuse axonal injury 7 (9.3)      
    Diffuse axonal injury and subdural haemorrhage 1 (1.3)      
    Subdural haemorrhage 1 (1.3)      
    Subdural effusion 1 (1.3)      
    Subarachnoid haemorrhage 1 (1.3)      
    Contusion and subdural haemorrhage 2 (2.7)      
    Contusion 2 (2.7)      
 Presence, n (%)
 
Duration
 
 Yes No Mean Median SD IQR 
Loss of consciousness (min) 28 (37.3) 47 (62.7) 0.9 2.2 0–1.0 
Post-traumatic amnesia (h) 69 (92.0) 6 (8.0) 2.66 1.5 3.4 0.1–4.5 
Retrograde amnesia (h) 17 (22.7) 58 (77.3) 0.4 1.7 
Disorientation 53 (70.7) 22 (29.3)     
Focal neurological deficit 17 (22.7) 58 (77.3)     
GCS, 15 points 69 (92.0) 6 (8.0)     
GCS, 14 points 6 (8.0) 69 (92.0)     
Acute traumatic lesion on CT* 7 (9.3) 68 (90.7)     
Acute traumatic lesion on MRI 15 (20.0) 60 (80.0)     
    Diffuse axonal injury 7 (9.3)      
    Diffuse axonal injury and subdural haemorrhage 1 (1.3)      
    Subdural haemorrhage 1 (1.3)      
    Subdural effusion 1 (1.3)      
    Subarachnoid haemorrhage 1 (1.3)      
    Contusion and subdural haemorrhage 2 (2.7)      
    Contusion 2 (2.7)      

*All traumatic lesions were also visible on MRI.

GCS = Glasgow Coma Scale; IQR = interquartile range.

Control subjects were patients evaluated in the emergency department of Tampere University Hospital who suffered ankle injuries. The same study criteria used with the mild TBI sample were applied in the enrolment of the controls when applicable. Control subjects were enrolled in an age and gender stratified manner, with five males and five females in the following age groups: (i) 18–30 years, (ii) 31–40 years, (iii) 41–50 years; and (iv) 51–60 years. All 40 control subjects underwent a head MRI with the same sequences as the mild TBI sample. With controls, all MRI findings were interpreted as normal.

Clinical assessment

A broad clinical assessment of the patients in the final sample was performed by T.L. The patients were interviewed regarding past health including diagnosed medical conditions, medication use, head injury history, alcohol consumption according to the Alcohol Use Disorders Identification Test (Saunders et al., 1993), and drug and narcotics abuse history. The presence and duration of retrograde amnesia and post-traumatic amnesia were assessed using the Rivermead Post-Traumatic Amnesia Protocol (King et al., 1997) together with the Galveston Orientation and Amnesia Test (GOAT) (Levin et al., 1979). All patients scored ≥80 points on the GOAT (normal 76–100). Persistent post-traumatic amnesia was screened using the revised Westmead Post-Traumatic Amnesia Scale (Shores et al., 1986) and all the patients scored a flawless 12 points at the time of assessment. Glasgow Coma Scale (Teasdale and Jennett, 1976) scores were collected from ambulance forms (if applicable) and the emergency department records (the lowest scores were recorded). The clinical assessment included a complete neurological examination. Participants were determined to have met ICD-10 diagnostic criteria for post-concussional syndrome if they endorsed symptoms on the Sports Concussion Assessment Tool (SCAT2) (McCrory et al., 2009) 22-item symptom scale in at least three of the ICD-10 symptom categories. The time duration criterion was not applied to the acute analyses. The SCAT2 was added later in the study, so only a subset of patients were administered the test (n = 51 at emergency department, and n = 50 at 1 month due to a patient dropping out of the follow-up evaluation).

Neuroimaging

In the emergency department, a non-contrast head CT was performed with a 64-row CT scanner (GE, Lightspeed VCT) for all consecutive patients with head injury. Head MRI was done with a 3 T MRI scanner (Siemens Trio). The MRI protocol included sagittal T1-weighted 3D inversion recovery prepared gradient echo, axial T2 turbo spin echo, conventional axial and high resolution sagittal FLAIR, axial T2*, axial susceptibility weighted, and diffusion weighted imaging series. Head MRIs were done within 14 days after injury (mean 5.8 ± 2.5 days). All head MRIs were analysed and systematically coded by two neuroradiologists.

The DTI data were collected by a single-shot, spin echo-based and diffusion-weighted echo planar imaging sequence. The parameters for the DTI sequence were repetition time 5144 ms, echo time 92 ms, field of view 230 mm, matrix 128 × 128, three averages, slice/gap 3.0/0.9 mm, and voxel dimension of 1.8 × 1.8 × 3.0 mm. Two b-factors were used, 0 and 1000 s/mm2 with 20 diffusion gradient orientations. A 12-channel head matrix coil was used. Finally, the signal to noise ratio value was well above the limit of acceptance for diffusion imaging, following the group’s previous work (Hakulinen et al., 2012).

Tract-based spatial statistics

Whole brain voxel-wise statistical analysis was carried out using TBSS (Smith et al., 2006), a part of FSL, version 5.0.1 (Smith et al., 2004). A threshold for fractional anisotropy values for the creation of the skeleton was chosen at ≥0.3 to exclude peripheral areas from the skeleton and to reduce bias in the results. Effects of age and gender were controlled by adding them as covariates of no interest (nuisance variables) to general linear model setup in non-matched group analysis. No covariates were used in the analyses with matched groups.

Statistical analysis

Non-parametric, permutation-based tests were carried out by Randomize (included in FSL) (Nichols and Holmes, 2002) with 5000 permutations and threshold-free cluster enhancement. To reduce experiment-wise type 1 errors associated with multiple comparisons, the threshold for statistical significance was set at P ≤ 0.01, adjusted for multiple comparisons. Two-sided, two-sample Wilcoxon-Mann-Whitney tests were used for each age- and gender-matched analysis to test the groups’ ages for significant differences.

Results

Effect of age on diffusion measures

In the control subjects, comparison of fractional anisotropy values among the four age groups yielded significantly (P ≤ 0.01) lower fractional anisotropy values in age groups 41–50 years and 51–60 years in comparison with control subjects aged between 18 and 30 years. The areas with fractional anisotropy deviations extended widely across the whole cerebral white matter (Fig. 1A). Statistically significant (P ≤ 0.01) differences in apparent diffusion coefficient values were obtained only in the oldest of the age groups, 51–60 years. Apparent diffusion coefficient values were elevated in the anterior parts of the right cerebral hemisphere (Fig. 1B).

Figure 1

Qualitative results of age effect on DTI measures; the mean fractional anisotropy skeleton is presented in green and laid on top of greyscale MNI152 1 mm T1 image. Colour coding is used to differentiate whether values on the skeleton increase (red–yellow) or decrease (blue–light blue) in the older age group. The youngest age group (18–30 years) is used as a reference for the other age groups in the analyses. Slice coordinates (MNI152 aligned anatomical) x = 15 mm, y = 20 mm, z = 23 mm. Radiological convention (Left is Right). AD = axial diffusivity; ADC = apparent diffusion coefficient; FA = fractional anisotropy; RD = radial diffusivity.

Figure 1

Qualitative results of age effect on DTI measures; the mean fractional anisotropy skeleton is presented in green and laid on top of greyscale MNI152 1 mm T1 image. Colour coding is used to differentiate whether values on the skeleton increase (red–yellow) or decrease (blue–light blue) in the older age group. The youngest age group (18–30 years) is used as a reference for the other age groups in the analyses. Slice coordinates (MNI152 aligned anatomical) x = 15 mm, y = 20 mm, z = 23 mm. Radiological convention (Left is Right). AD = axial diffusivity; ADC = apparent diffusion coefficient; FA = fractional anisotropy; RD = radial diffusivity.

Axial diffusivity was found to decrease with age. In the second oldest age group, 41–50 years, lower axial diffusivity values extended from the brainstem to the posterior part of the cerebrum. Similar areas of lower axial diffusivity were found in the oldest age group (Fig. 1C). Radial diffusivity values were increased in the two oldest age groups in a widespread manner (Fig. 1D). See Supplementary Table 1 for quantitative information for the age effect analyses.

Group comparison between controls and patients

The age- and gender-covaried TBSS analysis between the patients with mild TBI and controls did not reveal statistically significant differences in fractional anisotropy, apparent diffusion coefficient, axial diffusivity or radial diffusivity. For the age- and gender-matched analyses, 40 patients with mild TBI were carefully matched on age and gender to the 40 control subjects. No statistically significant differences in fractional anisotropy, apparent diffusion coefficient, axial diffusivity, or radial diffusivity were found.

The association between mild traumatic brain injury severity and diffusion measures

Subgroup analyses were conducted based on injury severity characteristics. For patients with a loss of consciousness for >5 min (n = 7) compared with matched control subjects, no statistically significant differences were found in fractional anisotropy, apparent diffusion coefficient, axial diffusivity or radial diffusivity. A subgroup of 25 patients with post-traumatic amnesia >3 h was compared with 25 matched control subjects. There were no statistically significant differences in fractional anisotropy, apparent diffusion coefficient, axial diffusivity or radial diffusivity. A subgroup of 15 patients with complicated mild TBIs (i.e. all had a trauma-related structural abnormality on CT and/or MRI) was compared to 15 matched control subjects. There were no statistically significant differences in fractional anisotropy, apparent diffusion coefficient, axial diffusivity or radial diffusivity. Six patients with Glasgow Coma Scale = 14 were compared to six matched control subjects. There were no statistically significant differences in fractional anisotropy, apparent diffusion coefficient, axial diffusivity, or radial diffusivity. The final subgroup (definite mild TBI) included patients with a combination of any of the previous criteria. This mild TBI group consisted of 29 patients who were compared to 29 matched controls. There were no statistically significant differences in fractional anisotropy, apparent diffusion coefficient, axial diffusivity or radial diffusivity.

Association between diffusion measures and post-concussional symptoms

TBSS was used to compare the post-concussional syndrome subgroups against age-matched patients who did not meet the ICD-10 symptom criteria for post-concussional syndrome at the acute stage (n = 20) or at 1 month post-injury (n = 12). Acutely, there were no statistically significant differences between post-concussional syndrome subgroups in fractional anisotropy, apparent diffusion coefficient, axial diffusivity or radial diffusivity. Moreover, there were no statistically significant differences in fractional anisotropy, apparent diffusion coefficient, axial diffusivity, or radial diffusivity in those who had, versus those who did not have post-concussional syndrome, at 1 month after injury.

Discussion

There were three objectives in this study: (i) to illustrate the extensive impact of ageing on brain white matter in adults between the ages of 18 and 60; (ii) to determine whether ‘pure’ acute mild TBI is associated with white matter changes detectable with TBSS; and (iii) to study the effects of mild TBI severity on DTI measures. No significant (P < 0.01) mild TBI-related DTI abnormalities were detectable, even with greater injury severity taken into account. Importantly, none of the DTI measures were significantly associated with post-concussional symptoms acutely or at 1 month after injury. Some of the mild TBI severity analyses resulted in nearly statistically significant results (Table 3). The results of this study would have been different if a liberal criterion for significance, with no consideration of multiple comparisons, was applied (i.e. P < 0.05). That is, some of the subgroup analyses would have been considered statistically significant. In contrast, statistically significant age-related axonal degradation was observed in the control group. This is in line with previous reports (Stadlbauer et al., 2008; Yoon et al., 2008) and further establishes that age should be considered carefully in white matter analyses.

Table 3

Group sizes, ages and minimum P-values of each Mild TBI analysis

 Mild TBI versus covaried controls Mild TBI versus matched controls LOC > 5 min versus matched controls PTA > 3 h versus matched controls Complicated Mild TBI versus matched controls GCS = 14 versus matched controls Definite mild TBI versus matched controls Mild TBI and PCS versus mild TBI and no PCS: acute Mild TBI and PCS versus mild TBI and no PCS: 1 month 
Sample sizes 75 versus 40 40 versus 40 7 versus 7 25 versus 25 15 versus 15 6 versus 6 29 versus 29 20 versus 20 12 versus 12 
Age, patients 37.2 ± 12.0 40.2 ± 11.6 35.9 ± 10.4 37.4 ± 11.6 35.9 ± 10.2 33.0 ± 10.1 38.0 ± 11.2 34.2 ± 10.6 36.3 ± 10.7 
Age, controls 40.6 ± 12.2 40.7 ± 12.1 35.9 ± 10.7 38.0 ± 11.4 36.3 ± 10.0 34.5 ± 10.1 40.0 ± 11.7 34.3 ± 10.5 36.2 ± 10.4 
Min. P-values          
Fractional anisotropy 0.131 0.185 0.158 0.166 0.306 0.212 0.162 0.123 0.320 
Apparent diffusion coefficient 0.102 0.118 0.059 0.022 0.186 0.157 0.031 0.102 0.247 
Axial diffusivity 0.086 0.189 0.039 0.023 0.021 0.268 0.045 0.220 0.324 
Radial diffusivity 0.102 0.118 0.079 0.069 0.330 0.158 0.054 0.070 0.297 
 Mild TBI versus covaried controls Mild TBI versus matched controls LOC > 5 min versus matched controls PTA > 3 h versus matched controls Complicated Mild TBI versus matched controls GCS = 14 versus matched controls Definite mild TBI versus matched controls Mild TBI and PCS versus mild TBI and no PCS: acute Mild TBI and PCS versus mild TBI and no PCS: 1 month 
Sample sizes 75 versus 40 40 versus 40 7 versus 7 25 versus 25 15 versus 15 6 versus 6 29 versus 29 20 versus 20 12 versus 12 
Age, patients 37.2 ± 12.0 40.2 ± 11.6 35.9 ± 10.4 37.4 ± 11.6 35.9 ± 10.2 33.0 ± 10.1 38.0 ± 11.2 34.2 ± 10.6 36.3 ± 10.7 
Age, controls 40.6 ± 12.2 40.7 ± 12.1 35.9 ± 10.7 38.0 ± 11.4 36.3 ± 10.0 34.5 ± 10.1 40.0 ± 11.7 34.3 ± 10.5 36.2 ± 10.4 
Min. P-values          
Fractional anisotropy 0.131 0.185 0.158 0.166 0.306 0.212 0.162 0.123 0.320 
Apparent diffusion coefficient 0.102 0.118 0.059 0.022 0.186 0.157 0.031 0.102 0.247 
Axial diffusivity 0.086 0.189 0.039 0.023 0.021 0.268 0.045 0.220 0.324 
Radial diffusivity 0.102 0.118 0.079 0.069 0.330 0.158 0.054 0.070 0.297 

All of the analyses, except the final column, suggest an increase in fractional anisotropy values and decrease in apparent diffusion coefficient, axial diffusivity and radial diffusivity values when compared with matched subjects. The post-concussional syndrome (PCS) analysis at 1 month suggested a decrease in fractional anisotropy and increase in apparent diffusion coefficient, axial diffusivity, and radial diffusivity values, although clearly non-significant. GCS = Glasgow coma score; LOC = loss of consciousness; PTA = post-traumatic amnesia.

In the group comparison between the control subjects and the patients with mild TBI, we used two different methods to manage the effects of age on white matter integrity: (i) covariance correction; and (ii) pair-wise age-matching. Previous studies suggest, in contrast with our results, abnormal DTI findings in acute mild TBI (Hulkower et al., 2013) (Table 1). Differences in the methodology and timing of MRI scanning can partly explain the difference between the present results and previous reports. Pathophysiological alterations after mild TBI are time-dependent, thus rendering chronology a crucial factor in the interpretation of DTI findings (Hulkower et al., 2013; Toth et al., 2013). For the acute stage of mild TBI, multiple publications illustrate an increase or decrease in fractional anisotropy values and decrease in apparent diffusion coefficient values (Hulkower et al., 2013; Toth et al., 2013). From a neurometabolic view, statistical unification of neuroimaging data obtained acutely (e.g. 2 days) and post-acutely (e.g. 1 month) after injury does not seem justifiable. More research is needed to better understand acute versus post-acute changes in DTI measures after mild TBI.

This study was larger and more tightly methodologically controlled than most previous DTI studies of acute mild TBI. The patients were clinically assessed by the same physician using structured instruments and strict clinical enrolment criteria were applied to both patient and control groups. A specific time interval was used in imaging to reduce the possible effect of diverse neurometabolic stages of mild TBI recovery.

The negative main outcome of this study can be partly caused by the statistical group-wise nature of TBSS. The current result does not rule out individualized patterns of focal white matter changes in different anatomical regions. Moreover, some of the non-significant findings in mild TBI severity subgroups may be explained by the lower statistical power of these analyses. For post-concussional syndrome analyses, patient matching was limited to age-matching due to lack of uniformity in gender distributions. Lack of gender-matching is a methodological limitation in the post-concussional syndrome analysis. Indeed, the heterogeneous nature of brain trauma, combined with methodological differences across studies, likely underlies the fact that the anatomical areas showing DTI differences vary considerably across past mild TBI studies. Therefore, TBSS as a supplement to region of interest analysis could be an appropriate alternative for a comprehensive DTI analysis.

In conclusion, in this large homogeneous, premorbidly healthy sample, acute mild TBI was not associated with obvious DTI abnormalities detectable with TBSS. Clear differences in DTI findings were associated with age, even in healthy subjects in their 40 s. Therefore, age should always be considered a potential confounder in DTI studies.

Acknowledgements

The authors thank research assistants Anne Simi and Marika Suopanki-Ervasti for their contribution in the data collection. A portion of this study was presented at the 10th International Workshop on Computational Systems Biology in June 2013 in Tampere, Finland. The study was also presented at the XXI World Congress of Neurology in September 2013 in Vienna, Austria.

Funding

This work was supported by a grant by the Emil Aaltonen foundation to T.I.

Conflict of interest

Grant Iverson, PhD has been reimbursed by the government, professional scientific bodies, and commercial organizations for discussing or presenting research relating to MTBI and sport-related concussion at meetings, scientific conferences, and symposiums. He has a clinical practice in forensic neuropsychology involving individuals who have sustained MTBIs. He has received honorariums for serving on research panels that provide scientific peer review of programs. The other authors report no competing or conflicts of interest.

Supplementary material

Supplementary material is available at Brain online.

Abbreviations

    Abbreviations
  • DTI

    diffusion tensor imaging

  • TBI

    traumatic brain injury

  • TBSS

    tract-based spatial statistics

References

Arfanakis
K
Haughton
VM
Carew
JD
Rogers
BP
Dempsey
RJ
Meyerand
ME
Diffusion tensor MR imaging in diffuse axonal injury
AJNR Am J Neuroradiol
 , 
2002
, vol. 
23
 (pg. 
794
-
802
)
Bazarian
JJ
Zhong
J
Blyth
B
Zhu
T
Kavcic
V
Peterson
D
Diffusion tensor imaging detects clinically important axonal damage after mild traumatic brain injury: a pilot study
J Neurotrauma
 , 
2007
, vol. 
24
 (pg. 
1447
-
59
)
Chu
Z
Wilde
EA
Hunter
JV
McCauley
SR
Bigler
ED
Troyanskaya
M
, et al.  . 
Voxel-based analysis of diffusion tensor imaging in mild traumatic brain injury in adolescents
AJNR Am J Neuroradiol
 , 
2010
, vol. 
31
 (pg. 
340
-
6
)
Hakulinen
U
Brander
A
Ryymin
P
Ohman
J
Soimakallio
S
Helminen
M
, et al.  . 
Repeatability and variation of region-of-interest methods using quantitative diffusion tensor MR imaging of the brain
BMC Med Imaging
 , 
2012
, vol. 
12
 pg. 
30
 
Henry
LC
Tremblay
J
Tremblay
S
Lee
A
Brun
C
Lepore
N
, et al.  . 
Acute and chronic changes in diffusivity measures after sports concussion
J Neurotrauma
 , 
2011
, vol. 
28
 (pg. 
2049
-
59
)
Holm
L
Cassidy
JD
Carroll
LJ
Borg
J
Neurotrauma task force on mild traumatic brain injury of the WHO collaborating centre. Summary of the WHO collaborating centre for neurotrauma task force on mild traumatic brain injury
J Rehabil Med
 , 
2005
, vol. 
37
 (pg. 
137
-
41
)
Hulkower
MB
Poliak
DB
Rosenbaum
SB
Zimmerman
ME
Lipton
ML
A decade of DTI in traumatic brain injury: 10 years and 100 articles later
AJNR Am J Neuroradiol
 , 
2013
, vol. 
34
 (pg. 
2064
-
74
)
King
NS
Crawford
S
Wenden
FJ
Moss
NE
Wade
DT
Caldwell
FE
Measurement of post-traumatic amnesia: how reliable is it?
J Neurol Neurosurg Psychiatry
 , 
1997
, vol. 
62
 (pg. 
38
-
42
)
Levin
HS
O'Donnell
VM
Grossman
RG
The galveston orientation and amnesia test. A practical scale to assess cognition after head injury
J Nerv Ment Dis
 , 
1979
, vol. 
167
 (pg. 
675
-
84
)
Lipton
ML
Gulko
E
Zimmerman
ME
Friedman
BW
Kim
M
Gellella
E
, et al.  . 
Diffusion-tensor imaging implicates prefrontal axonal injury in executive function impairment following very mild traumatic brain injury
Radiology
 , 
2009
, vol. 
252
 (pg. 
816
-
24
)
Luoto
TM
Tenovuo
O
Kataja
A
Brander
A
Ohman
J
Iverson
GL
Who gets recruited in mild traumatic brain injury research?
J Neurotrauma
 , 
2013
, vol. 
30
 (pg. 
11
-
6
)
Mayer
AR
Ling
J
Mannell
MV
Gasparovic
C
Phillips
JP
Doezema
D
, et al.  . 
A prospective diffusion tensor imaging study in mild traumatic brain injury
Neurology
 , 
2010
, vol. 
74
 (pg. 
643
-
50
)
McCrory
P
Meeuwisse
W
Johnston
K
Dvorak
J
Aubry
M
Molloy
M
, et al.  . 
Consensus statement on concussion in sport: the 3rd International Conference on Concussion in Sport held in Zurich, November 2008
Br J Sports Med
 , 
2009
, vol. 
43
 
Suppl 1
(pg. 
i76
-
i90
)
Messé
A
Caplain
S
Pelegrini-Issac
M
Blancho
S
Montreuil
M
Levy
R
, et al.  . 
Structural integrity and postconcussion syndrome in mild traumatic brain injury patients
Brain Imaging Behav
 , 
2012
, vol. 
6
 (pg. 
283
-
92
)
Miles
L
Grossman
RI
Johnson
G
Babb
JS
Diller
L
Inglese
M
Short-term DTI predictors of cognitive dysfunction in mild traumatic brain injury
Brain Inj
 , 
2008
, vol. 
22
 (pg. 
115
-
22
)
Nichols
TE
Holmes
AP
Nonparametric permutation tests for functional neuroimaging: a primer with examples
Hum Brain Mapp
 , 
2002
, vol. 
15
 (pg. 
1
-
25
)
Saunders
JB
Aasland
OG
Babor
TF
de la Fuente
JR
Grant
M
Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption–II
Addiction
 , 
1993
, vol. 
88
 (pg. 
791
-
804
)
Shores
EA
Marosszeky
JE
Sandanam
J
Batchelor
J
Preliminary validation of a clinical scale for measuring the duration of post-traumatic amnesia
Med J Aust
 , 
1986
, vol. 
144
 (pg. 
569
-
72
)
Smith
SM
Jenkinson
M
Woolrich
MW
Beckmann
CF
Behrens
TE
Johansen-Berg
H
, et al.  . 
Advances in functional and structural MR image analysis and implementation as FSL
Neuroimage
 , 
2004
, vol. 
23
 
Suppl 1
(pg. 
S208
-
19
)
Smith
SM
Jenkinson
M
Johansen-Berg
H
Rueckert
D
Nichols
TE
Mackay
CE
, et al.  . 
Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data
Neuroimage
 , 
2006
, vol. 
31
 (pg. 
1487
-
505
)
Stadlbauer
A
Salomonowitz
E
Strunk
G
Hammen
T
Ganslandt
O
Age-related degradation in the central nervous system: assessment with diffusion-tensor imaging and quantitative fiber tracking
Radiology
 , 
2008
, vol. 
247
 (pg. 
179
-
88
)
Teasdale
G
Jennett
B
Assessment and prognosis of coma after head injury
Acta Neurochir (Wien)
 , 
1976
, vol. 
34
 (pg. 
45
-
55
)
Toth
A
Kovacs
N
Perlaki
G
Orsi
G
Aradi
M
Komaromy
H
, et al.  . 
Multi-modal magnetic resonance imaging in the acute and sub-acute phase of mild traumatic brain injury: can we see the difference?
J Neurotrauma
 , 
2013
, vol. 
30
 (pg. 
2
-
10
)
Yallampalli
R
Wilde
EA
Bigler
ED
McCauley
SR
Hanten
G
Troyanskaya
M
, et al.  . 
Acute white matter differences in the fornix following mild traumatic brain injury using diffusion tensor imaging
J Neuroimaging
 , 
2013
, vol. 
23
 (pg. 
224
-
7
)
Yoon
B
Shim
YS
Lee
KS
Shon
YM
Yang
DW
Region-specific changes of cerebral white matter during normal aging: a diffusion-tensor analysis
Arch Gerontol Geriatr
 , 
2008
, vol. 
47
 (pg. 
129
-
38
)