Forty-four patients and 40 controls underwent MRI using a 3T Siemens Magnetom Total Imaging Matrix (TIM) Trio. The imaging protocol included diffusion tensor imaging, details of which are described below. Other sequences included a 3D T1
-weighted structural sequence (magnetization prepared rapid gradient echo), a fluid attenuated inversion recovery sequence, a gradient echo sequence and a dual spin echo (proton density/T2
-weighted) sequence. Patients were imaged at a minimum of 6 months post-injury, and were chosen from a larger cohort, selected because they did not exhibit any significant focal lesions (total lesion volume >1.5
in size) on fluid attenuated inversion recovery, T2
or gradient echo sequences. In addition, no patients had any focal lesions visible in the regions of interest used, on any of the structural MRI sequences employed. Ethical approval was obtained from the Research Ethics Committee and informed consent was obtained in all cases.
Comprehensive neuropsychological testing designed to test memory, executive function and attention was performed in all patients on the day of imaging. These tasks were part of the Cambridge Neuropsychological Automated Test Battery (CANTAB, www.camcog.com
) and were run on an Advantech personal computer (Model PPC-120T-RT), and included the Cambridge Gambling Task. Responses were registered by either a response key or the touch sensitive screen depending on the task, with the subjects using their dominant hand. Eighteen controls (who were not imaged) underwent identical neuropsychological testing. Six months was chosen as the minimum time post-injury, as previous analysis of neuropsychological data in a similar cohort of patients by our group has shown that the neuropsychological parameters assessed by the Cambridge Neuropsychological Automated Test Battery are stable after this time point (Salmond et al., 2006a
). Volunteers were recruited from appropriate participants already known to the department and by advertisements placed with ethics committee approval. Exclusion criteria included a prior history of contact with neurological or psychiatric services, previous illicit drug use or alcohol dependence.
To perform the Cambridge Gambling Task, subjects were presented with an array of 10 blue and red boxes, and given a bank of points to bet with. A token was hidden under one of the boxes. The subject was asked to guess under which colour the token was hidden and to wager a proportion of their points on that decision. These wagers were offered in an ascending and descending sequence, in order to differentiate impulsive responses from genuine risk preference, as here the subject must wait to place their bets in the ascend condition. The ratio of blue to red boxes provides the outcome probabilities of winning and losing explicitly, thus allowing the task to assess decision-making under risk rather than ambiguity. The results of the Cambridge Gambling Task were broken down into five components: (i) rational choices, the proportion of trials where the majority colour was chosen; (ii) deliberation time, the latency to make a colour choice; (iii) amount bet, the average across conditions and box ratios (higher bets are assumed to indicate risk preference); (iv) impulsivity index, the difference in percentage bet in descending versus ascending conditions. Consistently early bets (e.g. 95% points descending—5% points ascending) produce a high impulsivity index; and (v) risk adjustment index, quantifies bet calibration across ratios [2
(% bet 9:1)
(% bet 8:2)
(% bet 7:3)
(% bet 6:4)]/average % bet, so higher scores imply better risk adjustment (Deakin et al., 2004
In order to help control for Type 1 errors, previous imaging studies were used to select the regions of interest before commencing the imaging analysis. However these imaging studies (Rogers et al., 1999b
; Rubinsztein et al., 2001
; Ersche et al., 2005
), many of which involved PET, chiefly assessed neuroanatomical involvement of cortical areas, and provided little information on the deep grey matter structures involved in a task. Consequently, the regions of interest specified were expanded by the inclusion of regions based on theoretical knowledge about the circuits proposed to be involved in a particular task. The regions of interest included medial prefrontal cortex, ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, superior frontal gyrus, orbitofrontal gyrus, frontal white matter, hippocampus, insular cortex, thalamus, striatum (dorsal and ventral) and the caudate. In order to detect whether any associations that were detected simply reflected the overall burden of injury, we also sought correlations between task performance in areas that would not be expected to be involved in any aspect of the task, the parietal cortex and the posterior corpus callosum so as to provide negative control regions.
As the majority of regions of interest hypothesized to be involved in the Cambridge Gambling Task contained mainly grey matter, the apparent diffusion coefficient should be more sensitive to the detection of pathological changes than fractional anisotropy. For this reason, and to minimize the number of comparisons, the apparent diffusion coefficient was prospectively chosen as the outcome measure for this study. It has been shown that the use of multiple b
-values for a smaller number of unique gradient directions provides apparent diffusion coefficient results that are more robust than ones obtain with a higher number of sampling directions but only one b
-value (Correia et al., 2009
). Therefore, we used a sequence with multiple b
-values. The diffusion tensor imaging parameters were as follows: 12 non-collinear directions, five b
-values ranging from 338 to 1588
, five b
0 images, acquisition matrix size 96
96, field of view 192
mm, 63 axial slices, 2
mm slice thickness, repetition time
ms, echo time
ms. All scans were visually inspected prior to analysis, and subjects (two patients, four controls) who had moved more than two voxels (4
mm) during the diffusion sequence were removed prior to data analysis. The final dataset was therefore composed of 42 patients and 38 controls.
The diffusion tensor imaging data underwent eddy current correction and apparent diffusion coefficient maps were created using the Oxford Centre for functional MRI of the brain (FMRIB’s) Diffusion Toolbox and all the b
-values were used in the calculation of the tensor model (http://www.fmrib.ox.ac.uk/fsl
). To aid coregistration, the skull and extracranial soft tissue were stripped from the magnetization prepared gradient echo images using the Brain Extraction Tool (Smith, 2002
). The diffusion weighted data were normalized using a two-step approach. First, all patient and control magnetization prepared gradient echo images were coregistered to the MNI152 template using the vtkCISG normalized mutual information algorithm (http://www.image-registration.com
). The b
0 image was subsequently coregistered to the subject’s own magnetization prepared gradient echo image. The transformation matrix normalizing the magnetization prepared gradient echo image was then applied to the b
These regions of interest were manually drawn on the high resolution, high signal-to-noise Colin27 template (Holmes et al., 1998
) using Analyse 7.0 (http://www.mayo.edu/bir
). This image was chosen as unlike the averaged MNI152 templates, it has enough anatomical detail and contrast necessary to trace regions of interest. All coregistered images were visually inspected to ensure that regions of interest corresponded to the regions specified and/or were not affected by distortion artefact and manually adjusted if they did not. This approach was used to reduce the bias associated with completely hand drawn regions of interest, while mitigating coregistration errors that are inherent in a fully automated approach. The mean apparent diffusion coefficient for the different regions of interest was calculated using in-house software (written by GBW). To ensure the intra-rater reliability of the regions of interest, two regions—the posterior corpus callosum and the thalamus—were completely reanalysed without referring back to the previous adjusted regions of interest and the intra-class correlation coefficients calculated. Apparent diffusion coefficients in each of the regions of interest identified from prior knowledge of task neuroanatomy were compared with performance on the cognate task components.
Statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS 14.0, Chicago, IL, USA, http://www.spss.com
). Following assessment of the data for normality, parametric and non-parametric comparisons were performed where appropriate. Mann–Whitney U-test was used for unpaired tests and the Wilcoxon signed rank test for paired comparisons. Partial correlations were used to control both for time to scan post-injury (days) and age at scan (years). Spearman rank correlations were used for non-parametric correlations. To correct for multiple comparisons for the correlations between the regions of interest and the behavioural measures the false discovery rate was calculated which resulted in a P
-value of 0.012 (Genovese et al., 2002
). For other comparisons P
0.05 was accepted as significant.