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 age-

matched 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 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. 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 go-

trials, 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/change-

trials). 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’ intra-

individual 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 whole-

brain 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 full-

width half-maximum Gaussian kernel, pre-whitening using FILM and temporal high-

pass 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 high-

resolution 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, re-

binarized, 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 Low-

PM 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.

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