Supplementary material
Supplementary Experimental Procedures
Neuropsychological assessment
A battery of standardized neuropsychological tests sensitive to cognitive impairments
commonly observed following TBI was used. The Wechsler Test of Adult Reading
(WTAR) (Wechsler, 2001) provided an estimate of pre-morbid intellectual
functioning. Current verbal and nonverbal reasoning ability was assessed using
Wechsler Abbreviated Scale of Intelligence (WASI) Similarities and Matrix
Reasoning subtests (Wechsler, 1999). Verbal Fluency Letter Fluency (F-A-S) and
Colour-Word Interference (Stroop) tests were administered from the Delis-Kaplan
Executive Function System (D-KEFS) to assess word generation fluency, cognitive
flexibility, inhibition and set-shifting (Delis et al. , 2001). The Trail Making Test
(Forms A and B) was used to further assess executive functions (Reitan, 1958). The
Digit Span (DS) subtest of the Wechsler Memory Scale-Third Edition (WMS-III) was
included in the battery to assess working memory (Wechsler, 1997). The Logical
Memory I and II subtests of the WMS-III were included to obtain a measure of
immediate and delayed recall of structured verbal material. The People Test (PT) from
the Doors and People Test battery was used as a measure of associative learning and
recall (immediate and delayed) (Baddeley et al. , 1994). The two baseline conditions
of the D-KEFS Stroop test (Colour Naming and Word Reading) and the TMT-A are
also sensitive to impairments in information processing speed. TBI subjects were also
asked to complete a Frontal Systems Behavior (FrSBe) questionnaire (Grace et al. ,
1999). The FrSBe gives a measure of pre-TBI and post-TBI dysexecutive symptoms
as assessed by the patient and an observer who knows the patient well. 14/23 of the
Low-PM and 21/40 of the High-PM patients fully completed both questionnaires. The
Hospital Anxiety and Depression Scale (HADS) was used to measure the intensity
and frequency of mood symptoms (Zigmond and Snaith, 1983), often seen following
TBI (Jorge and Robinson, 2003).
Control groups
Controls were age-matched to the patients at the group level. For the resting state
functional connectivity analyses, 24 age and gender matched healthy volunteers were
recruited (16 males, mean age 36.2 ± 10.2 years). A separate young control group of
19 subjects (12 male, mean age 24.5 ± 2.75 years) was used to define the reference
networks, which were used in the analysis of both the patient and age-matched control
groups. For the analysis of brain activation during the stop-signal task, another group
of 25 age-matched subjects were used (17 males, mean age 34.8 ± 9.6 years). An agematched group of 20 subjects was used as a control group for neuropsychological
comparison (11 male, age-range 19-60, mean age 35.75 ± 11 years). Finally, a group
of eleven healthy volunteers were recruited (4 males, mean age 30.4 ± 7.7 years) to
define the normal range of ability on the stop-change task.
Stop-change task (SCT)
Performance monitoring was investigated behaviorally using the SCT (Fig 2A)
(Bekker et al. , 2005, Verbruggen and Logan, 2008, Verbruggen and Logan, 2009).
During go-trials subjects were presented with a series of arrows in the centre of the
screen pointing to either the right (>>>) or left (<<<). The direction of the arrows
determined the direction of subjects’ finger press response, i.e. arrows pointing to the
right were responded to with the right-hand and vice versa. During Change-trials the
arrows appeared, just as in the go-trials, then after a variable delay (the change-signal
delay (CSD)) the arrows changed direction. Subjects were instructed to respond to the
final direction of the arrows. If they responded incorrectly then they were instructed to
correct themselves immediately by making the appropriate response. Normally
subjects correct all or nearly all of their errors. The failure to correct errors provides
an explicit measure of impaired performance monitoring. Explicit feedback about
performance accuracy was not provided
All subjects performed one run of the SCT after a training session. The run consisted
of 204 pseudo-randomly ordered trials with an inter-stimulus interval of 1.75 seconds.
Seventy percent were go-trials, 20% change-trials and 10% rest-trials. For go-trials a
fixation-cross appeared for 350ms before the arrow cues were presented (either <<<
or >>>). The arrows were on screen for a further 1400ms before the next trial started.
On change-trials the arrows appeared and then, following a change-signal delay
(CSD) of at least 200ms, the arrows changed direction. The CSD was adaptive and
changed according to a staircase procedure determined by each subject’s performance
(described below). During SCT training subjects were explicitly instructed to correct
any mistakes made during the task by pressing the correct key after recognizing their
error. Furthermore, to ensure that the failure to perform this aspect of the task was not
simply due to misunderstanding the instructions, any subjects that failed to correct any
of their errors were excluded from analyses (n=2). Subjects tend to slow down in
order to avoid making errors (Falkenstein et al. , 2000). The importance of speed was
emphasized in two ways: firstly, all subjects were instructed to answer as quickly and
accurately as possible; secondly, a visual “Speed Up!” cue provided negative
feedback if subjects responded outside of a variable time limit (see below).
Change-signal delay (CSD): The CSD is designed to produce errors of inhibition. The
larger the CSD the more difficult it is for a subject to inhibit their initial response. In
order to produce a consistent number of errors between subjects an adaptive staircase
procedure manipulates the CSD depending upon a subject’s performance. Each
subject starts with a CSD of 200ms. After the first two change-trials, if a subject is
more than 50% accurate on the change-trials the CSD is increased by 50ms with no
upper limit. Similarly, if a subject is less than 50% accurate on the change-trials the
CSD is decreased by 50ms with a lower limit of 250ms.
Negative feedback: To produce more errors, and avoid a situation where subjects
simply wait for either the arrows to change direction or the appearance of the stopsignal, a negative feedback cue was presented when subjects’ responses were
particularly slow. The feedback was a visual cue. The phrase “Speed Up!” appeared
on screen for 1400ms immediately after the slow trial. Slow trials were defined as a
response that fell outside a variable time limit. The time limit was established for each
subject at the start of the SCT and SST runs based upon the subject’s reaction times in
the previously performed choice-reaction task (CRT). The initial time limit was set as
the 95% percentile of a subject’s correct reaction times in the CRT. As subjects
performed the SCT and SST, this time limit was adapted to include correct go-trials
from the ongoing tasks.
Stop-signal task (SST)
The neural dynamics of performance monitoring and error processing were further
investigated using fMRI analysis of the SST. This was completed in a subgroup of 38
patients and 25 controls.
SST Low-PM patient subgroup: Eighteen patients (ten males, mean age 39.9 ±11.7,
range 21-67 years). Average time elapsed since their brain injury at the time of
scanning was 25.5 ± 23.7 months (range 2-101 months).
SST High-PM patient subgroup: Thirty patients (twenty males, mean age 34.6
±11.5, range 18-54 years). Average time elapsed since their brain injury at the time of
scanning was 20.7 ± 33.4 months (range 2-168 months). The two groups were well
matched in terms of age, gender, time elapsed since injury, and injury severity.
Stop-signal Task design
The SST is a test of response inhibition similar in many respects to the SCT. The key
difference is that the SST does not require an alternative motor response or a
correction response making it more amenable to fMRI analysis. All subject performed
two runs of the SST in the MRI scanner. Each run consisted of 184 pseudo-randomly
ordered trials with an inter-stimulus interval of 1.75 seconds. 70% of trials were gotrials, 20% stop-trials and 10% rest-trials. During go-trials a fixation-cross appeared
for 350ms before the arrow cues were presented (either <<< or >>>). The arrows were
on screen for a further 1400ms before the next trial started. On stop-trials the arrows
appeared and following a short stop-signal delay (SSD) the stop-signal (a red circle)
appeared slightly above them (Fig 2B). The SSD was adaptive and changed according
to a staircase procedure determined by each subject’s ongoing performance (see
below). As with the SCT speed was emphasized in the task instructions and in explicit
adaptive negative feedback during the task. Any subject with less than 30% success
on stop-trials was excluded from the analysis (one subject was removed from the
Low-PM group for this reason).
SSD: The stop-signal is designed to produce a consistent number of response
inhibition errors across subjects. The SSD was changed using the same specifications
as the adaptive staircase procedure used to alter the CSD (see above).
Negative feedback: Just as in the SCT, in order to produce more errors, and avoid a
situation where subjects simply wait for the appearance of the stop-signal, a negative
feedback cue was presented when subjects’ responses were particularly slow. The
feedback was a visual cue. The phrase “Speed Up!” appeared on screen for 1400ms
immediately after the slow trial. The method for calculating hen negative feedback
should be provided is the same as for the SCT (see above).
Behavioural analysis
Baseline reaction times were calculated as a rolling average of the last ten successful
go-trials for a subject during that run (excluding incorrect trials and stop/changetrials). This measure is used to accommodate for attentional drift and trial-to-trial
variability across the runs (Ham et al. , 2012). All post-error slowing behavioural
values are calculated as differences from the baseline reaction time. Subjects’ intraindividual variability in reaction time was calculated as the standard deviation divided
by the mean of the reaction times for all correctly answered go-trials for that run.
MRI Scanning
MRI data were obtained using a Philips (Best, The Netherlands) Intera 3.0 Tesla MRI
scanner using Nova Dual gradients, a phased array head coil, and sensitivity encoding
(SENSE) with an under-sampling factor of 2. Functional MRI images were obtained
using a T2*-weighted gradient-echo echoplanar imaging (EPI) sequence with wholebrain coverage (TR/TE = 2000/30; 31 ascending slices with thickness 3.25 mm, gap
0.75 mm, voxel size 2.19×2.19×4 mm, flip angle 90°, field of view 280×220×123
mm, matrix 112×87). Quadratic shim gradients were used to correct for magnetic field
inhomogeneities within the brain. T1-weighted whole-brain structural images were
also obtained in all subjects (78 contiguous slices; slice thickness 2.3 mm; repetition
time (TR) = 30 msecs; echo time (TE) = 16 msecs; field of view (FOV) 220 x 220 x
180 mm, matrix = 192 x 190; flip angle = 12 °; resolution 0.92 x 0.92 x 2.3 mm).
Lesion masks were created manually from the T1 images and converted into 1mm3
standard space using FLIRT tool in the FMRIB Software Library (Jenkinson et al. ,
2002). Whole-brain functional MRI data from the choice-reaction task were analyzed
with standard random effects general linear models using the FMRIB Software
Library
Paradigms were programmed using Matlab® Psychophysics toolbox (Psychtoolbox-3
www.psychtoolbox.org) and stimuli presented through an IFIS-SA system (In Vivo
Corporation). Responses were recorded through a fiber optic response box
(Nordicneurolab, Norway), interfaced with the stimulus presentation PC running
Matlab.
Diffusion tensor imaging (DTI) acquisition
Diffusion-weighted volumes with gradients applied in 16 non-collinear directions
were collected in each of the four DTI runs, resulting in a total of 64 directions. The
following parameters were used: 73 contiguous slices, slice thickness = 2 mm, field of
view (FOV) 224 mm, matrix 128 x 128 (voxel size = 1.75 x 1.75 x 2 mm3), b value =
1000, and four imaging runs with no diffusion weighting (b = 0 s/mm2).
Data Analysis
Functional connectivity analysis
Reference components from the output of the temporal concatenation ICA were
selected for further analysis. In total twenty-five independent group components were
produced (Supplementary Fig.S1). Twelve of these were well-recognized brain
networks, although their precise nomenclature varies between publications. In
addition to these networks the ICA also produced thirteen components that
corresponded to physiological noise and movement artifacts. The Fronto-Parietal
Control Network was largely symmetrical and contained the dorsal anterior cingulate
and bilateral insulae, often described as the Salience network (Seeley et al. , 2007), as
well as bilateral temporo-parietal junction, bilateral middle and inferior frontal gyri
(Supplementary Fig. S1A). These regions have previously been shown to be involved
in error processing (Ham et al. , 2012). The Visual network was largely symmetrical
and extended from the dorsal superior frontal gyri to the lateral occipital cortex
(Supplementary Fig. S1B).
Task functional MRI
Imaging analysis was performed using FEAT (FMRI Expert Analysis Tool) version
5.98, a part of FSL version 4.1.2 (FMRIB’s Software Library; www.fmrib.ox.ac.uk/fsl
(Smith et al. , 2004)). The first four volumes acquired were discarded to allow for T1
equilibrium effects. Image pre-processing involved realignment of EPI images to
remove the effects of motion between scans, spatial smoothing using a 5 mm fullwidth half-maximum Gaussian kernel, pre-whitening using FILM and temporal highpass filtering using a cut-off frequency of 1/50 Hz to correct for baseline drifts in the
signal. FMRIB's Linear Image Registration Tool (FLIRT) was used to register EPI
functional datasets into standard MNI space using the participant’s individual highresolution anatomical images.
FMRI data were analyzed using voxel-wise time series analysis within the framework
of the General Linear Model (GLM). To this end a design matrix was generated with
a synthetic hemodynamic response function and its first temporal derivative. Task and
rest blocks were modeled in the design matrix. Mixed effects analysis of session and
group effects was carried out using FLAME (FMRIB’s Local Analysis of Mixed
Effects (Beckmann et al. , 2003). Final statistical images were thresholded using
Gaussian Random Field based cluster inference with a height threshold of Z>2.3 and a
cluster significance threshold of P<0.05.
Diffusion tensor imaging analysis
Diffusion-weighted images were registered to the b = 0 image by affine
transformations to minimize distortion due to motion and eddy currents, and then
brain-extracted using the Brain Extraction Tool (BET) (Smith, 2002), part of the FSL
image processing toolbox (Smith et al. , 2004). DTI parameter maps were generated
using FMRIBs Diffusion Toolbox (FDT) in FSL for fractional anisotropy (FA).
Region of interest analysis was performed using probabilistic tracts derived from a
healthy control group using a technique described previously (Bonnelle et al. , 2012)
and shown to better estimate white-matter integrity in TBI (Squarcina et al. , 2012).
Peaks from the ICA derived neural networks were used as the seeds for probabilistic
tractography in the healthy controls. Tracts were created for: the FPCN, connecting
the dACC to the right and left insulae; and, for the visual network connecting the right
and left occipital lobes to the right and left precuneus respectively. Once tracts had
been created in all of the controls they were converted into standard 1mm MNI space
and averaged. The average tract was then thresholded at a p-value of 0.5 and
binarized. These tracts were then transformed into each subjects’ individual space, rebinarized, and multiplied with the FA of the individual subject using fslmaths. The
mean FA value for the tract was then calculated from the mean of all non-zero voxels
included in that mask, using fslstats.
Supplementary Experimental Results
Sub-analysis of moderate/severe patients
Low-PM subjects have reduced functional connectivity within the FPCN
Compared to the High-PM sub-group, Low-PM patients again showed reduced
functional connectivity within the FPCN. Whole-brain analysis demonstrated that the
dACC, right IFG, and MFG were significantly less functionally connected to the rest
of the FPCN in the Low-PM group (Supplementary Fig. S3A). Region of interest
analysis assessing key nodes of the FPCN and VN confirmed that there were no
significant differences between High-PM patients and controls in either network.
Sub-analysis of moderate/severe patients
Low-PM subjects showed increased insulae activation following errors
The direct contrast between neural activity on incorrect stop-trials and go-trials was
repeated to investigation the neural correlates of error processing. Again Low-PM
patients showed greater activation of the left insula and parietal operculum compared
to the High-PM sub-group (Supplementary Fig. S3B). The distribution of the LowPM patients’ excess activity was almost identical to that seen in the main analysis.
Once again, no regions showed increased activity in the High-PM or control groups
compared to Low-PM group following errors.
High-PM subjects showed increased activation in the right MFG following errors
The High-PM group again showed greater activation than controls in the right MFG
and bilateral putamen and the left caudate nucleus after errors (Supplementary Fig.
S3C). The distribution of the increased activation seen on subgroup analysis was
almost identical to the main analysis.
Structural imaging results
Even after removing the milder TBI patients there were still no significant differences
in between patient groups in terms of lesion load, lesion distribution or DTI metrics.
References
Baddeley AD, Emslie H, Nimmo-Smith I. Doors and People Test: A Test of Visual
and Verbal Recall and Recognition. Bury-St-Edmunds: Thames Valley Test
Company; 1994.
Beckmann CF, Jenkinson M, Smith SM. General multilevel linear modeling for group
analysis in FMRI. NeuroImage 2003; 20:1052-63.
Bekker EM, Overtoom CC, Kenemans JL, Kooij JJ, De Noord I, Buitelaar JK, et al.
Stopping and changing in adults with ADHD. Psychol Medicine 2005; 35:807-16.
Bonnelle V, Ham TE, Leech R, Kinnunen KM, Mehta MA, Greenwood RJ, et al.
Salience network integrity predicts default mode network function after traumatic
brain injury. P Natl Acad Sci U S A 2012; 109:4690-5.
Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in
the brain. Nat Rev Neurosci 2002; 3:201-15.
Delis DC, Kaplan E, Kramer JH. Delis-Kaplan Executive Function System. San
Antonio: San Antonio, TX: Psychological Corporation; 2001.
Falkenstein M, Hoormann J, Christ S, Hohnsbein J. ERP components on reaction
errors and their functional significance: a tutorial. Biol Psychol 2000; 51:87-107.
Grace J, Stout JC, Malloy PF. Assessing frontal lobe behavioral syndromes with the
frontal lobe personality scale. Assessment 1999; 6:269-84.
Ham TE, de Boissezon X, Leff A, Beckmann C, Hughes E, Kinnunen KM, et al.
Distinct Frontal Networks Are Involved in Adapting to Internally and Externally
Signaled Errors. Cereb Cortex 2012.
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust
and accurate linear registration and motion correction of brain images. Neuroimage
2002; 17:825-41.
Jorge R, Robinson RG. Mood disorders following traumatic brain injury. Int Rev
Psychiatry 2003; 15:317-27.
Menon V. Large-scale brain networks and psychopathology: a unifying triple network
model. Trends Cogn Sci 2011; 15:483-506.
Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A
default mode of brain function. P Natl Acad Sci U S A 2001; 98:676-82.
Reitan R. The validity of the Trail Making test as an indicator of organic brain
damage. Percept Motor Skill 1958; 8:271-6.
Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al.
Dissociable intrinsic connectivity networks for salience processing and executive
control. J Neurosci 2007; 27:2349-56.
Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17:14355.
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; 23 Suppl 1:S208-19.
Squarcina L, Bertoldo A, Ham TE, Heckemann R, Sharp DJ. A robust method for
investigating thalamic white matter tracts after traumatic brain injury. Neuroimage
2012.
Verbruggen F, Logan GD. After-effects of goal shifting and response inhibition: a
comparison of the stop-change and dual-task paradigms. Q J Exp Psychol 2008;
61:1151-9.
Verbruggen F, Logan GD. Models of response inhibition in the stop-signal and stopchange paradigms. Neurosci Biobehav R 2009; 33:647-61.
Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL. Evidence for a
frontoparietal control system revealed by intrinsic functional connectivity. J
Neurophysiol 2008; 100:3328-42.
Wechsler D. Wechsler Memory Scale- Third Edition: Administration and Scoring
Manual. San Antonio: TX: Psychological Corporation; 1997.
Wechsler D. WASI: Wechsler Abbreviated Scale of Intelligence. San Antonio: TX:
The Psychological Corporation; 1999.
Wechsler D. Wechsler Test of Adult Reading. San Antonio: TX: The Psychological
Corporation; 2001.
Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr
Scand 1983; 67:361-70.