Methods

Participants

Twenty-two right-handed participants with moderate to severe TBI (mean age = 41.4 ± 13.2 (standard deviation [S.D.]) years; education = 14.6 ± 1.9 (S.D.) years; mean time since injury = 79.5 ± 51.0 (S.D.) months) and 20 healthy control participants (age = 37.7 ± 10.9 (S.D.) years; education = 15.3 ± 1.8 (S.D.) years) participated in the study. The majority of the individuals in the TBI group had sustained their injury as a result of a motor-vehicle accident (59%), followed by falls (32%), followed by assault (9%). The groups did not differ in age or education. The groups were marginally different in terms of gender (14 men in the TBI group, 8 men in the HC group; χ 2 (1) = 3.6, p = 0.057). While this was not a robust difference, we nevertheless included gender as a covariate in all analyses. Twenty of the 22 participants in the TBI group had sustained a moderate-to-severe TBI, defined as the lowest Glasgow Coma Scale (GCS) rating in the first 24 hours following injury being below 1333. When a GCS score was not available, subjects were included only when there was sufficient medical documentation that allowed for a post-hoc estimation of initial GCS, or if other confirmatory data (e.g., positive anatomic neuroimaging findings, focal neurologic signs) were available. Two subjects in the TBI group were included who were considered to have had complicated mild TBIs. One participant had a GCS of 15, but had sustained a subarachnoid hemorrhage at the time of the accident. The other had a GCS of 14, but had sustained a subdural hematoma, intra-parenchymal hemorrhage, and had cerebral edema as a result of the accident. Because the results did not change when the data from these participants was excluded, their data were retained in the sample.

For both the TBI and HC groups, subjects were: 1) free of a history of prior neurological insult or disease (other than TBI for the TBI group) (e.g., stroke, seizures, or brain tumor); 2) free from significant psychiatric history (such as schizophrenia or bipolar disorder) due to the potential influence of such disorders on cognitive functioning (assessed by self-report corroborated by medical records when available); 3) right handed due to the effect of mixed hand dominance on cerebral organization; 4) free of alcohol or drug abuse history. Subjects currently taking benzodiazepines, neuroleptics, or psycho-stimulants were excluded due to the potential effects of these medications on cognition and the hemodynamic response. For all study participants, additional exclusionary criteria associated with MRI (ferrous metal in the body) were discussed and strictly enforced.

All subjects participated in two scanning sessions which, for expository purposes, we will refer to as Experiment 1 and 2. In one session, subjects performed the N-Back working memory task (see below for details) and in the other they performed the modified symbol-digit modalities task (SDMT; see Experiment 2). The order in which subjects performed these tasks was counterbalanced (see Fig. 1).

Figure 1 A flow diagram of the experimental design. Thirty-six individuals with TBI and 33 HCs were enrolled. After exclusions were applied (e.g., MRI incompatibility, psychiatric history, claustrophobia), the sample was reduced to 32 individuals with TBI and 31 HCs. Subjects then participated in a day of neuropsychological (NP) testing. Three subjects did not complete the NP test battery (1 TBI, 2 HC), and because the focus of the current study was on functional neuroimaging, these subjects were retained in the sample. Subjects were then randomized into two groups. One group performed the N-Back test while in the fMRI scanner on Day 2 and the SDMT task in the scanner on Day 3. For the other group, the order of N-Back and SDMT tasks was reversed. There was some attrition during this three-day experiment, resulting in a final sample of 31 individuals with TBI and 28 HCs. After applying data quality control (e.g., excluding subjects with excessive motion) and matching the two groups on Age and Education, the final sample was 22 individuals with TBI and 20 HCs. Full size image

The Institutional Review Boards of Kessler Foundation and Rutgers University Medical School approved the study (which included Experiments 1 and 2), and the study was performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all subjects.

Scan session 1

A 3-Tesla Siemens Allegra scanner was used to acquire all neuroimaging data. Behavioral data acquisition, randomization and stimulus presentation was administered using the E-Prime software34. The N-Back paradigm was presented in the scanner in counterbalanced order across participants in an event-related design. A T2*-weighted pulse sequence was used to collect functional images in 32 contiguous slices during eight blocks (four at each of two difficulty levels), resulting in 140 acquisitions per block (echo time = 30 ms; repetition time = 2000 ms; field of view = 22 cm; flip angle = 80°; slice thickness = 4 mm, matrix = 64 × 64, in-plane resolution = 3.438 mm2). A high-resolution magnetization prepared rapid gradient echo (MPRAGE) image was also acquired (TE = 4.38 ms; TR = 2000 ms, FOV = 220 mm; flip angle = 8°; slice thickness = 1 mm, NEX = 1, matrix = 256 × 256, in-plane resolution = 0.859 × 0.859 mm), and was used to normalize the functional data into standard space. A fluid attenuation inversion recovery (FLAIR) image was acquired to assess lesion load (TE = 81 ms; TR = 8530 ms; FOV = 256 × 320 mm; flip angle = 180°; slice thickness = 4 mm; matrix = 320 × 154; in-plane resolution = 0.688 × 0.688 mm).

Behavioral paradigm

All participants completed a series of practice trials before scanning, exposing them to the two difficulty levels of the N-Back task. During the fMRI scan, participants were presented with the N-Back working memory task in which task difficulty was varied by presenting the 0-back condition, which places a relatively low load on working memory, and the 2-back condition, which places a higher load on working memory. There were 4 blocks of each level of the N-Back task (8 blocks total), with 65 trials per block (16 of which were targets). The 4 blocks of each task were always presented together (that is, the two tasks were not interleaved), and the order of presentation (0-back first vs. 2-back first) was counterbalanced across subjects. During the 0-back task, participants were asked to respond each time the target letter “K” was presented on the screen, while during the 2-back task, participants were asked to respond when the target letter corresponded to the letter presented two trials before. In all cases, the letter stimuli remained on the screen for 1.5 sec. and there was an inter-trial interval of 500 ms.

Visual Analog Scale of Fatigue

To evaluate the level of on-task ‘state’ fatigue, participants were presented with a visual analogue scale of fatigue before and after each block of the N-Back task. Participants were asked: “How mentally fatigued are you?” and were asked to indicate their level of fatigue on a scale form 0 to 100, with 0 being not fatigued at all and 100 being extremely fatigued. The visual analogue scale (VAS) has historically been used as a tool for capturing subjective ratings of sensations and feelings or moods. The VAS has been studied with various clinical populations and has demonstrated moderate to high validity and reliability35. Previous fMRI studies have utilized the VAS to assess energy and fatigue levels12, 19, perceived pain36, and changes in mood or emotional intensity19. Lee et al. developed the VAS-F, a visual analogue scale for capturing fatigue severity that consisted of fatigue and energy subscales37. In order to mask the purpose of the study, five additional VASs were administered as well, in randomized order. These assessed happiness, sadness, pain, tension and anger.

Questionnaires

After the completion of the fMRI procedure, participants filled out the Fatigue Severity Scale (FSS), Modified Fatigue Impact Scale (MFSI) and Chicago Multiscale Depression Inventory (CMDI). These questionnaires provided a measure of trait fatigue and depression.

Data analysis

Behavioral data

The response time (RT) and accuracy data were each analyzed with linear mixed effects models using the R statistical analysis package (version 1.0.136). The between-subjects factor was Group (TBI vs. HC) and the within-subjects factors were Task (0-back vs. 2-back) and Block (block 1–4 of each task; this was included to account for any order effects, because the four blocks of each task were run sequentially). Three covariates were also included: fatigue, gender and lesion load (see below). The fatigue score used for each block was the average of the VAS-F score reported before and after each block. Because the VAS-F scores differed between the groups, the scores were centered separately for each group prior to analysis. The behavioral data from eight HCs and two individuals with TBI was lost due to equipment failure during scanning (their neuroimaging data is included in the neuroimaging analyses).

The ratings from the visual analog scale of fatigue (VAS-F) were analyzed with a 2 × 4 × 2 mixed, between- and within- subjects ACNOVA, with gender as a covariate. The within-subjects factors were Task (0-back vs. 2-back), and Block (block 1, block 2, block 3, block 4). The between-subjects factor was Group (TBI vs. HC).

fMRI data

For each of four fMRI blocks, the first 5 images were discarded to ensure steady state magnetization. All images were preprocessed using Analysis of Functional NeuroImages (AFNI)38. The realignment, co-registration and normalization were done in a single transform. This was accomplished by calculating and saving the parameters necessary for realignment using 3dvolreg (i.e., the spatial co-registration of all images in each time-series to the first image of the series). Next, the parameters necessary to co-register the first image in each time-series with the high resolution MPRAGE were calculated and saved (using 3dAllineate). Third, the MPRAGE image (1 × 1 × 1 mm voxels) was warped into standard Talairach space using a non-linear warping algorithm (3dQwarp), and the warping parameters were saved. Finally, the transforms necessary to realign, co-register and warp the data into standard space were combined and applied to the functional time-series data in a single transformation. The images were then smoothed using an 8 × 8 × 8 mm Gaussian smoothing kernel (using 3dBlurToFWHM), and scaled to the mean intensity (using 3dcalc). Each of the four blocks of each task (0-back and 2-back) were then deconvolved separately (using 3dDeconvolve). Motion parameters and two polynomial regressors (to model signal drift) were included as regressors of no interest.

Subject motion: Because it has been shown that clinical populations tend to move more in the MRI scanner than HCs39, we analyzed the motion parameters with two between- and within-subjects ANOVAs: one for translational motion and one for rotational motion. For the analysis of translational motion, the between-subjects factor was Group (TBI vs. HC) and the within-subjects factors were Task (0-back vs. 2-back), Run (Run 1–4), and Movement (displacements in the superior/inferior direction, the left/right direction, and the anterior/posterior direction). For the analysis of rotational motion, the factors were the same, but the levels of the factor Movement were roll, pitch and yaw. Both analyses showed a significant effect of Group (translation: F(1,39) = 4.34, p < 0.05; rotation: F(1,39) = 4.95, p < 0.05), and in both cases, this was because the TBI group moved more than the HC group (translation: 0.46 mm for TBIs vs. 0.25 mm for HCs; rotation: 0.45° for TBIs vs. 0.28° for HCs). Because subject motion translates directly into increased noise in the fMRI dataset, these results suggest that the TBI data were noisier than the HC data. In order to control for this, we employed the ‘data scrubbing’ technique proposed by Power et al.40, 41 in which a ‘framewise displacement’ (FD) is computed for each acquisition. The FD is the sum of the absolute values of the derivatives of the translational and rotational realignment estimates, and is used in two ways: it is included in the deconvolution of each subject’s data as a regressor, and it is used to censor out acquisitions where subjects’ motion was excessive (defined as ≥ 0.5 FD). This was done for all subjects, in both groups, and resulted in the removal of 1.90% of TRs in the HC group and 5.33% of TRs in the TBI group.

Lesion load: Because our sample was moderate/severe TBI, we assessed lesion load in all of our TBI subjects. This was done by manually tracing lesions evident on the FLAIR image using the Jim 6 medical image display package (Xinapse Systems, England). The volume of each lesion was calculated by interpolating across slices; these were then summed to derive total lesion load for each subject42.

The fMRI data were analyzed with the same linear mixed effects model as was used for the RT and accuracy data. This model included a between-subjects factor of Group (TBI vs. HC), within-subjects factors of Task (0-back vs. 2-back) and Block (block 1–4 of each task), and the three covariates of fatigue, gender and lesion load. Two follow-up analyses were then run to investigate each group separately. For these analyses, the factors and covariates were the same, with the exception of Group, which was omitted. All group-level statistical maps were thresholded using both the alpha level and cluster size correction (extent of activation). The alpha level was set at p < 0.01 and the cluster size was set at 108 contiguous voxels. The results of Monte Carlo simulations showed that this combination resulted in a corrected alpha level of p < 0.05. Because we had a prior hypothesis about the involvement of the caudate, a separate Monte Carlo simulation was conducted on just the caudate. Based on this, clusters of at least 26 voxels within the caudate were also considered significant.