Title: Stratifying drug treatment of cognitive impairments after traumatic brain injury using neuroimaging

Authors: Peter O. Jenkins PhD1, Sara De Simoni PhD1, Niall J. Bourke MSc1, Jessica

Fleminger MEng1, Gregory Scott PhD1, David J. Towey PhD2, William Svensson2, Sameer

Khan2, Maneesh C. Patel3, Richard Greenwood MD FRCP4, Daniel Friedland1, Adam

Hampshire1, James H. Cole PhD1, David J. Sharp PhD1

Affiliations:1Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, Division of Brain Sciences, Hammersmith Hospital, London, UK.2Department of Nuclear Medicine, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK.3Imaging Department, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK.4Institute of Neurology, Division of Clinical Neurology, University College London, London, UK.

Correspondence to:

David J Sharp The Computational, Cognitive and Clinical Neuroimaging Laboratory (C3NL)Division of Brain SciencesDepartment of MedicineImperial College LondonDu Cane RoadW12 0NNLondon, UK

Email: david.sharp@imperial.ac.uk

Running Head: Methylphenidate treatment after TBI

Abstract:

Cognitive impairment is common following traumatic brain injury. Dopaminergic drugs can

enhance cognition after traumatic brain injury, but individual responses are highly variable.

This may be due to variability in dopaminergic damage between patients. We investigate

whether measuring dopamine transporter levels using 123I-ioflupane SPECT predicts response

to methylphenidate, a stimulant with dopaminergic effects.

40 moderate-severe traumatic brain injury patients with cognitive impairments completed a

randomized, double-blind, placebo-controlled, crossover study. 123I-ioflupane SPECT, MRI

and neuropsychological testing were performed. Patients received 0.3mg/kg of

methylphenidate or placebo twice a day in two-week blocks. Subjects received

neuropsychological assessment after each block and completed daily home cognitive testing

during the trial. The primary outcome measure was change in choice reaction time produced

by methylphenidate and its relationship to stratification of patients into groups with normal

and low dopamine transporter binding in the caudate.

Overall, traumatic brain injury patients showed slow information processing speed. Patients

with low caudate dopamine transporter binding showed improvement in response times with

methylphenidate compared to placebo (median change = -16ms; 95% confidence interval

[CI], [-28, -3ms]; P=0.02). This represents a 27% improvement in the slowing produced by

traumatic brain injury. Patients with normal dopamine transporter binding did not improve.

Daily home-based choice reaction time results supported this: the low dopamine transporter

group improved (-19ms; 95% CI [-23, -7ms]; P=0.002) with no change in the normal

dopamine transporter group (P=0.50). The low dopamine transporter group also improved on

self-reported and caregiver apathy assessments (P=0.03 and P=0.02 respectively). Both

groups reported improvements in fatigue (P=0.03 and P=0.007).

The cognitive effects of methylphenidate after traumatic brain injury were only seen in

patients with low caudate dopamine transporter levels. This shows that identifying patients

with a hypodopaminergic state after traumatic brain injury can help stratify the choice of

cognitive enhancing therapy.

Introduction

The global costs of traumatic brain injury (TBI) have recently been estimated at $400 billion

annually (Maas et al., 2017). These are largely due to the long-term effects of injuries, with

cognitive impairment a significant cause of long-term disability. Treatment options for these

post-traumatic cognitive problems are very limited, with previous trials showing highly

variable responses across patients (Jenkins et al., 2018). A major issue is the heterogeneous

nature of TBI pathology and the impact that this has on an individual’s response to drug

treatment. A stratified approach to treating cognitive impairments is needed, where

medications are tailored based on the underlying pathophysiology (Maas et al., 2017).

Cognitive impairments are an important cause of persistent disability following TBI

(Whitnall et al., 2006). They prevent return to work and normal social activities with huge

socio-economic costs (Gustavsson et al., 2011). Cognitive enhancers are sometimes used to

treat these problems. The stimulant methylphenidate has the most evidence for its use after

TBI (Jenkins et al., 2016) and is widely used to treat cognitive impairments in other

conditions such as attention deficit hyperactivity disorder. Its primary mechanism of action is

blockade of the noradrenaline and dopamine transporters (Solanto, 1998), but it also increases

dopamine release via D2 receptor dependent modulation of vesicular trafficking (Volz et al.,

2007; Volz et al., 2008). These mechanisms increase extracellular levels of both

noradrenaline and dopamine, which is the primary mechanism by which methylphenidate

improves cognition (Berridge et al., 2006).

TBI can disrupt the dopamine system. Cell loss occurs in the substantia nigra following

cortical injury, with a 25% reduction in dopaminergic neurons in the substantia nigra

observed in one animal model (van Bregt et al., 2012). This produces a hypodopaminergic

state, with reduction in dopamine release and clearance seen after injury (Wagner et al.,

2005). The primary regulator of synaptic dopamine levels is the dopamine transporter (DaT)

and compensatory reductions in DaT can act to maintain synaptic dopamine levels. Two-

week administration of methylphenidate in an animal model of TBI has been shown to

increase striatal dopamine release, in part through an effect on DaT expression (Wagner et

al., 2009).

In some clinical studies, methylphenidate improves information processing speed, a core

deficit following TBI (Jenkins et al., 2016) that is strongly related to poor outcome (Ponsford

et al., 2008). However, individual responses to methylphenidate are highly variable, and there

has been no way to predict who will respond well to treatment (Jenkins et al., 2016). This is

problematic given the potential side effects of treatment. The variability in treatment

response is likely to be partly due to the heterogeneity in traumatic injuries across patients.

Patients typically have highly variable patterns of injury. We have recently shown that the

dopaminergic system shows varying amounts of damage after TBI, with differing degrees of

damage to the nigrostriatal system between individuals (Jenkins et al., 2018). Moreover, the

effect of neuromodulators on cognition is non-linear (Cools and D'Esposito, 2011). Previous

work shows that dopamine levels display an ‘inverted-U’ shaped relationship with cognitive

performance, with both low and high levels impairing performance (Cools and D'Esposito,

2011). The strongest evidence for this inverted-U relationship is seen in tasks measuring

cognitive control and working memory, domains in which dopamine is known to play an

important role (Cools and D'Esposito, 2011), but animal studies also show this relationship in

attentional performance (Granon et al., 2000). Previous studies in TBI show that impairments

in reaction time tasks are a consequence of impaired processing speeds as well as attentional

deficits, as patient responses become more variable as the task progresses (Bonnelle et al.,

2011). Therefore, determining an individual’s dopamine ‘status’ is likely to inform the

likelihood they will respond to dopaminergic therapies, as this response will depend on their

position on the ‘inverted-U’ relating dopamine levels and performance.

Dopamine transporter (DaT) levels in the striatum can be assessed in vivo using 123I-ioflupane

single-photon emission computed tomography (SPECT) scans, which are commonly used in

the diagnosis of Parkinson’s disease (Wenning et al., 1998). Low DaT levels indicate a

hypodopaminergic state and around 20% of moderate-severe TBI patients show obvious

radiological evidence of DaT abnormalities on 123I-ioflupane scanning. The caudate is most

affected post-TBI and low DaT levels in this region are associated with greater cognitive

impairment (Jenkins et al., 2018). Caudate DaT levels have also been shown to relate to

cognitive functioning in both healthy individuals (Mozley et al., 2001) and patients with

Parkinson’s disease (Marie et al., 1999; Muller et al., 2000).

Here we performed a randomized placebo-controlled trial of methylphenidate treatment for

cognitive impairment after TBI, as indexed by change in information processing speed. We

investigated whether individual variability in post-traumatic damage to the nigrostriatal

dopaminergic system influences response to two weeks of daily methylphenidate

administration. Molecular neuroimaging with 123I-ioflupane SPECT was used to quantify

dopaminergic abnormality in the striatum. We tested the hypothesis that patients with low

caudate DaT, indicating a hypodopaminergic state, would show greater cognitive

improvement following administration of methylphenidate. We also investigated whether the

effect of methylphenidate on response times followed an inverted-U shaped relationship as

seen in other more complex cognitive tasks. This approach is an example of biomarker driven

clinical trial design and is a proof-of-principle that characterising the integrity of

neurotransmitter systems after TBI can inform the choice of cognitive enhancing medications

and that the large degree of heterogeneity in the underlying pathophysiology following TBI

necessitates stratification of patients for treatment selection.

Materials and Methods

Study Oversight

The study was approved by the West London and GTAC NRES Committee (14/LO/0067)

and registered with ClinicalTrials.gov (NCT02015949). All participants provided written

informed consent. All authors reviewed and approved the manuscript and assume full

responsibility for the accuracy and completeness of the data and for the fidelity of this report

to the study protocol.

Study Population

This single-center study recruited from specialist TBI clinics in London, UK (Fig. 1). Eligible

patients were adults aged 20-65 with a history of a single moderate-severe TBI (Mayo

classification) (Malec et al., 2007) at least 3 months prior and a subjective complaint of

cognitive difficulties. Exclusion criteria included a significant neurological or psychiatric

illness diagnosed prior to the TBI and contraindication to methylphenidate. Full entry criteria

in Table S1 and demographics in Table S2, Supplementary Appendix.

Trial Design

A randomised, double-blind, placebo-controlled, crossover trial design was used. The

crossover design allowed subjects to act as their own control, thereby reducing the variance

in outcome measures. As patients were in the chronic phase, cognitive impairments were

considered to be relatively static over the course of the trial. In addition, methylphenidate is a

short-acting stimulant with a pharmacokinetic half-life of 2 to 3 hours (Kimko et al., 1999)

and therefore carryover effects were deemed to be minimal. As a crossover design was used,

a complete case analysis approach was adopted.

40 TBI patients completed the study (Fig. 1). After enrolment, the TBI patients and 20 age-

and gender-matched healthy control subjects had a baseline 123I-ioflupane SPECT scan, MRI

and neuropsychological assessment. After baseline assessment, the TBI patients were

randomised using a block design (block-size 4) into one of two treatment groups: two weeks

of placebo with crossover to two weeks of methylphenidate or two weeks of methylphenidate

with crossover to two weeks of placebo. Patients received either 0·3mg/kg rounded to the

nearest 5mg (maximum 25mg) of methylphenidate or placebo twice daily (morning, midday).

Study drugs were prepared as 5mg tablets of methylphenidate and matching placebo (Clinical

Trials Manufacturing and Supplies Department, Royal Free London NHS Trust).

Subjects had full neuropsychological assessment, including the choice reaction time (CRT)

task, at the study centre at the end of each two-week period. Subjects were also asked to

complete the CRT on a daily basis on a tablet computer at home during the four-week

treatment period. Subjects were instructed to complete the task once a day, between one and

four hours after taking the medication having been trained on the use of the tablet at the study

centre prior to the start of the trial.

123I-ioflupane Single-Photon Emission Computed Tomography and Magnetic resonance

imaging:

All participants had a 123I-ioflupane SPECT scan prior to entering the treatment trial. Before

administration of 123I-ioflupane, patients received potassium iodide tablets (2x60mg) to

minimize radiation exposure to the thyroid gland. One hour later, a bolus intravenous

injection of 123I-ioflupane (GE Healthcare Ltd) was administered (mean activity 185MBq).

SPECT images for all subjects were acquired using the same dual-headed gamma camera

(Symbia T16, Siemens Healthcare) at 180 minutes post-injection with LEHR collimators,

128x128 matrix, 1·45 zoom, 128 projections, and 30 seconds per projection.

Participants also had a T1-weighted high-resolution MPRAGE scan: 160 1-mm-thick

transverse slices, TR = 2300 ms, TE = 2·98 ms, FA = 9°, in-plane resolution = 1 x 1mm,

matrix size = 256 x 256, field of view = 25·6 x 25·6 cm.

Outcome Measures

Primary outcome measure

The pre-specified primary outcome measure was change in median response time on the CRT

task on methylphenidate compared to placebo and its relationship to specific binding ratio

(SBR) of DaT in the caudate, measured using 123I-ioflupane SPECT. The CRT task was

conducted at study visits at the end of each 2-week treatment. The CRT task is a two-choice

response time test where participants press a left or right button as fast as possible in response

to a left- or right-pointing arrow displayed on a screen. 168 trials were presented with a ratio

of 5:5:4 for right arrow:left arrow:fixation cross. Subjects responded with a right or left

button press in response to the arrow direction. The interstimulus interval was fixed at 1·75

seconds and the trial lasted in total 4·9 minutes. The task was completed on a laptop

computer. The CRT was chosen as the primary outcome measure as it has a high test-retest

reliability (Jenkins et al., 2015) and is sensitive to attentional and processing speed

impairments after TBI (Bonnelle et al., 2011), both of which are core deficits with strong

relationships to clinical outcomes post-TBI (Ponsford et al., 2008; Bonnelle et al., 2011).

Dopamine transporter levels

Responses on the CRT were stratified by caudate DaT levels. This was calculated from the

123I-ioflupane SPECT scans using a semi-quantitative analysis approach. Acquired data were

reconstructed with an ordered-subsets expectation maximization (OSEM) based iterative

algorithm (HybridRecon, HERMES Medical Solutions; Stockholm, Sweden) including

corrections for attenuation, scatter, and resolution. The reconstructed SPECT images were

then transformed into standard Montreal Neurological Institute 152 (MNI) space as described

previously (Jenkins et al., 2018) (Fig. S1, Supplementary Appendix).

Standard semi-quantitative analysis of the 123I-ioflupane SPECT scans was performed by

calculating the ratio of uptake in the caudate relative to the nonspecific uptake in the occipital

cortex. Uptake ratios were defined as the SBR ([caudate counts – occipital counts]/occipital

counts). A potential limitation of semi-quantitative analysis is the requirement that the uptake

in the occipital cortex is non-specific and not altered between groups. The occipital cortex is

devoid of DaT binding sites, therefore the uptake in this region is taken to reflect non-specific

uptake and is the standard approach for 123I-ioflupane SPECT scan analysis (Seibyl et al.,

1997). In addition, we confirmed there was no difference in occipital uptake between patients

and controls (t(38.5)=-1.38, p=0.18) to make sure that TBI did not have an unexpected effect

on occipital uptake. Patients were split into ‘low’ and ‘normal’ caudate DaT levels based on a

pre-specified cut-off of DaT levels less than or more than one standard deviation below the

mean of the control group, respectively.

Secondary outcome measures

In addition, the CRT task was performed daily at home on a tablet computer during the four-

week trial period. 170 trials were presented with a ratio of 5:5:4 for right arrow:left

arrow:fixation cross. The interstimulus interval was fixed at 1·75 seconds and the trial lasted

4·95 minutes. The task was completed on a tablet computer (iPad). This provided a second

measure of information processing speed that was highly related to our primary outcome

measure.

During visit assessments we measured performance on other neuropsychological tests

(Kinnunen et al., 2011) and behavioural questionnaires. The Trail Making Test (TMT) and

Delis-Kaplan Executive Function System (D-KEFS) Color-Word Interference Test (Stroop)

assessed information processing speed and executive function (Delis et al., 2001); the People

Test measured episodic memory (Wechsler, 1945); the Wechsler Abbreviated Scale for

Intelligence (WASI) Matrix Reasoning and Test of Adult Reading (WTAR) assessed

reasoning ability and premorbid IQ respectively (Wechsler, 1945).

Behavioural outcomes were assessed with the following: Lille Apathy Rating scale (LARS)

(Sockeel et al., 2006), Visual Analogue Scale for Fatigue (VAS-F) (Lee et al., 1991),

Glasgow Outcome scale – extended (GOSE) (Wilson et al., 1998), Hospital Anxiety and

Depression Scale (HADS) (Zigmond and Snaith, 1983), Frontal Systems Behaviour Scale

(FrSBe) (Grace and Malloy, 2001), Cognitive Failures Questionnaire (Broadbent et al.,

1982). Caregivers also completed the following: Lille Apathy Rating scale (Sockeel et al.,

2006), Frontal Systems Behaviour Scale (FrSBe) (Grace and Malloy, 2001), Cognitive

Failures Questionnaire (Broadbent et al., 1982), and Rating Scale of Attentional Behaviour

(Ponsford and Kinsella, 1991).

Statistical Analysis

The sample size (n=40) was pre-defined based on power calculations using prior work. A

prior study of 123I-ioflupane SPECT imaging in TBI patients suggested that <10 subjects

would be necessary to find reliable differences in DaT binding (Donnemiller et al., 2000).

Analysis of methylphenidate effects on cognitive function has been reported with effect sizes

(Cohen’s d) of >0.44 for a range of neuropsychological and behavioural measures (Whyte et

al., 2004). This indicates group sizes between 30-40 patients would be adequate to detect an

effect of methylphenidate across the whole group with a significance level of 5% and power

of 80%. Specifically, an effect size of methylphenidate on response time of 0·48 was reported

(Whyte et al., 2004), providing power of ~90% at 5% significance for a group size of 40. We

anticipated that patient stratification based on 123I-ioflupane SPECT would increase our

power to detect the effects of methylphenidate on cognition, but prior data was not available

to power for this specifically.

Patients were grouped according to whether they had low or normal caudate DaT levels.

Outcome measures between the methylphenidate and placebo visits for these two groups

were compared using Wilcoxon signed-rank tests (within group assessment) and Wilcoxon

rank-sum tests (between group assessment) due to non-normal distribution of the data. A

complete case analysis was conducted i.e. for each analysis only cases with relevant outcome

data were included. Imputation of missing data was not performed. Outliers were removed

based on a limit of three standard deviations above or below the mean. Three participants

were removed from the primary CRT analysis: one participant was an outlier based on

response time, one had excess errors and one excess missed responses (see Fig. S2,

Supplementary Appendix). A further participant was removed due to equipment failure on

testing day. Two participants were outliers on the TMT and two on the D-KEFS Color-Word

Interference Test. A two-sided P-value of 0·05 was considered to indicate statistical

significance. To assess the effect of outlier removal a sensitivity analysis was conducted with

outliers included.

For assessment of home neuropsychological tests, the mean score over each two-week period

was taken. To be included in the analysis, participants needed to complete at least four

separate valid sessions in each block after outlier removal. Valid sessions occurred on

separate days and within one to four hours of taking the treatment. The mean of the repeated

measures was taken for each block. A minimum of four measures per block was chosen as

previous analysis showed that the reliability of the CRT (assessed by the intra-class

correlation coefficient) rises from 0·84 for one measure to 0·96 if averaged over four

measures (Jenkins et al., 2015). Outliers were again removed based on a limit of three

standard deviations above or below the mean. 24 data points from 7 different patients were

removed as outliers from 830 completed tests. The overall compliance was 72%, with 806

completed tests out of a maximum 1120. 32 patients (17 in the normal caudate DaT group

and 15 in the low caudate DaT group) were included in this analysis. To assess the effect of

outlier removal a sensitivity analysis was conducted with outliers included.

To test for the presence of an inverted-U shaped relationship between performance and

dopamine levels (Fig. 2D) we used a Spearman’s correlation between the change in response

times and caudate SBR Z-values (calculated using the mean and standard deviation from the

control group). If an inverted-U relationship between performance (response time) and DaT

levels exists, then a negative, linear relationship between change in response times (i.e. in

Fig. 2D) and DaT levels would be expected. As illustrated in Fig. 2D, lower DaT levels will

produce a larger positive and higher DaT levels a more negative if an inverted-U

relationship exists between absolute performance and DaT levels. By using Z-scores for

caudate DaT levels (based on healthy controls), if the mean caudate DaT levels for healthy

controls provides the ‘optimum’ performance levels (i.e. the top of the inverted-U curve),

then the change in performance line should pass through the origin at this point (as the

change in performance will be zero as the slope/gradient is flat at the point of inflexion). Note

that smaller response times equate to improved performance and so for response times we

would expect a U-shaped relationship and hence a positive linear relationship between

change in response times and dopamine levels.

Results

Patients

From 10th July 2014 to 29th September 2016, 1525 patients were assessed for eligibility, 158

were screened and 46 enrolled into the study. Six patients withdrew after enrollment, leaving

40 patients with persistent cognitive complaints at least six months after a TBI who

completed the study (Fig. 1). Twenty patients received methylphenidate first and 20 placebo

first.

Demographic and clinical characteristics were similar in each crossover arm and between the

low and normal caudate SBR groups (Table 1). The median time since injury was 36 months

(IQR 102·5 months, range 6-366 months), mean age ± s.d. = 40 ± 12 years, and 85% were

males. The TBI group showed impairments at baseline across the neuropsychological tests

and behavioural questionnaires compared to the control group (Table 1).

Patients show reduced 123I-ioflupane specific binding ratios in the caudate

As previously reported, our TBI patients showed reduced DaT levels in the caudate compared

to a set of age-matched healthy controls (Fig. 2A-C) (Jenkins et al., 2018). Our hypothesis

was that change in information processing speed after methylphenidate would relate to an

individual’s position on an inverted U-shaped curve relating dopamine level to cognitive

function. Increasing dopamine in patients with a hypodopaminergic state would have

beneficial effects, whereas increasing levels in patients with high levels of dopamine might

be detrimental (Fig. 2D). Therefore, we divided patients into two groups with low and

normal caudate dopamine using a pre-specified cut-off of DaT levels in the caudate as greater

than one standard deviation below the mean of the control group (Fig. 2C).

Methylphenidate improves information processing speed in patients with caudate

dopamine transporter abnormalities

For the primary end point of response time, there was a significant improvement in the low

caudate DaT group during methylphenidate treatment compared to placebo (median change =

-16ms; 95% confidence interval [CI], -28 to -3ms; P=0·02) (Table 2 and Fig. 3A). There was

no significant change in the normal caudate DaT group (1ms; 95% CI [-10, 10ms]; P=0·84).

Direct comparison of low and normal caudate DaT groups showed improvement in response

times was significantly greater in the low-binding group (W=96, P=0·049). At baseline, the

patients were on average 58·5ms slower than the controls (Table 1), therefore a 16ms

improvement equated to a 27% improvement in response speed.

There was no significant difference in drug ordering between the low and normal caudate

DaT groups (47% vs. 52% received methylphenidate first respectively). Across all patients,

there was no statistically significant difference in response times between those taking

methylphenidate in the first block and those taking it in the second block (W=114, P=0·13).

In addition, across the whole patient group, there was no improvement in CRT on

methylphenidate compared to placebo (W=434, P=0.11).

There was no speed/accuracy trade-off associated with changes in response time seen on

methylphenidate. Errors and misses on CRT performance were similar for methylphenidate

and placebo in both the normal and low caudate DaT groups (Table S1, Supplementary

Appendix). If outliers were not removed from the analysis, the effect of methylphenidate in

the low caudate DaT group compared to placebo was of borderline significance (95% CI [-25

to 3ms]; P=0·06). The normal caudate DaT group still showed no significant change (95% CI

[-12, 8ms]; P=0·92) and direct comparison of low and normal caudate DaT groups did not

show a difference between the groups (W=135, P=0·15).

In addition to testing patients in the laboratory, we also conducted daily home CRT

assessment using tablet devices. This provided a complementary assessment of information

processing speed assessed at many more time points. This confirmed that the effect of

methylphenidate was only seen in the low caudate DaT group. Patients with low caudate DaT

showed a significant improvement in response times on methylphenidate compared to

placebo (-19ms; 95% CI [-23, -7ms]; P=0·002). Again, there was no significant change in

response time in the normal caudate DaT group (6ms; 95% CI [-10, 9ms]; P=0·50). Direct

comparison of low and normal caudate DaT groups again showed that improvement in

response times was significantly greater in the low DaT group (W=53, P=0·004) (Fig. 3B).

Methylphenidate improves apathy in patients with caudate dopamine

abnormalities

Patients with low caudate DaT also showed significant improvements in self-reported apathy

(LARS-self) (median change = -2 points; 95% CI [-9, 0]; P=0·03), as well as on caregiver-

reported apathy (LARS-other) (-3.5 points; 95% CI [-7, 0]; P=0·02). Patients with normal

caudate DaT did not show improvements in either apathy measure, although self-reported

apathy approached significance (-1 point; 95% CI [-6·5, 0·5]; P=0·07 and -0·5 points; 95%CI

[-10·0, 7·5]; P=0·98, respectively). Self-reported fatigue (VAS-F) was reduced in both the

low and normal DaT groups (median change = -7·5; 95% CI [-23·4, -3·2]; P=0·007 and -6·6;

95% CI [-18·6, -0·7]; P=0·03, respectively) (Table 2 and Fig. 3C). Methylphenidate did not

significantly affect any of the other cognitive or behavioural measures (Table 2).

Relationship between baseline caudate 123I-ioflupane specific binding ratio and

change in choice reaction time

To further explore the proposed inverted-U shaped relationship between performance and

dopamine levels (Fig. 2D), we examined the relationship between change in response time

and baseline caudate SBR level. If an inverted-U shaped relationship exists, the change in

response time between placebo and methylphenidate () will have a linear relationship to

baseline caudate SBR. For patients with low caudate SBR, methylphenidate would be

expected to speed responses i.e. a positive performance in Fig. 2D and a negative change in

CRT in Fig. 4. In contrast, patients with high caudate SBR would show a slowing of response

times on methylphenidate i.e. a negative performance in Fig. 2D. Our data supported the

existence of a U-shaped relationship between response times and dopaminergic state. In the

home assessment data, the change in response time produced by methylphenidate showed a

significant positive correlation with age-corrected caudate SBR Z-scores (rs=0·485, p=0·005).

This is a positive relationship as a decrease in response time equates to improved

performance (Fig. 4). On visit assessments the relationship approached significance

(rs=0·307, p=0·07). In addition, the change in performance at the mean caudate DaT level for

healthy controls (caudate DaT Z-score=0) is close to zero (see Fig. 4). This supports the

hypothesis that performance is optimized at this caudate DaT level. These results show that

reducing levels of caudate dopamine are associated with increasingly positive cognitive

effects of methylphenidate. In contrast, negative effects on performance can be produced

when caudate dopamine levels are high.

Adverse events

There were no serious adverse events. One participant discontinued methylphenidate and

withdrew from the trial due to unpleasant feelings of restlessness that were considered likely

secondary to the treatment. Heart rate was significantly increased on methylphenidate

compared to placebo (median change = 5·5 beats per minute; 95% CI [3, 12]; P<0·001).

Systolic blood pressure was not different between methylphenidate and placebo (median

change = 1·5mmHg; 95% CI [-2·5, 8]; P=0·21).

Discussion

Our biomarker driven clinical trial showed that the cognitive and behavioural effects of

methylphenidate after TBI are distinct in patients with normal and low DaT (123I-ioflupane)

SPECT binding. Patients with low DaT binding in the caudate showed improvements in

information processing speed (our primary outcome measure) and apathy, whilst those with

normal binding did not. Reduced DaT binding is a marker of a hypodopaminergic state after

TBI. Therefore, our findings provide a proof-of-principle that measuring the integrity of the

neurotransmitter system upon which a cognitive enhancer acts can help to stratify the

selection of cognitive treatment after TBI.

Although cognitive problems are common after TBI, their cause is multifactorial. Therefore,

it is unlikely that one medication will improve cognitive impairments in all patients. Instead,

it is rational to personalize pharmacological treatments of cognitive impairment by selecting

drugs that are most likely to be effective. Previous studies of cognitive enhancement after

TBI have provided very variable results, limiting their clinical impact (Jenkins et al., 2016).

Our findings show this is partly due to the effect of individual variation in TBI

pathophysiology on treatment response. Our study was powered based on previous work that

did not stratify patients (Whyte et al., 2004) to detect an effect of methylphenidate on

processing speed. Importantly, we found no overall effect on our primary outcome measure

across the whole TBI group. Therefore, if our trial had been conducted without patient

stratification, methylphenidate would have been judged ineffective in the treatment of post-

TBI cognitive and behavioural impairments. This illustrates the importance of considering

patient stratification when designing treatment trials in TBI.

The effect of methylphenidate on information processing speed we observed is likely to be

clinically relevant. Slowed thinking is one of the most frequent symptoms reported after TBI.

Objective impairments of processing speed are also common (Jenkins et al., 2016) and are

associated with poor clinical outcomes (Ponsford et al., 2008). Although processing speed is

a simple cognitive measure, rapid processing is a fundamental element of efficient cognitive

function. Cognitive performance is generally degraded when processing speed is slow,

because relevant operations cannot be rapidly executed and the products of early processing

may not be available when later processing is complete (Salthouse, 1996). Hence, the ~25%

improvement in processing speed deficit after TBI may impact generally on cognitive

function. In addition, both patient and caregiver ratings of apathy were significantly improved

in the low DaT binding group but not in the normal group, suggesting a likely role for

disruption to the dopaminergic system post-TBI in the causation of apathy. This is also an

important clinical finding, as apathy is a core deficit after TBI and is associated with worse

outcome and impaired engagement with rehabilitation (Ciurli et al., 2011).

Previous work investigating methylphenidate as a cognitive enhancer following TBI have not

examined the cause of highly variable responses (Jenkins et al., 2016; McDonald et al.,

2016). This is problematic because TBI produces variable damage within the

catecholaminergic systems upon which the drug acts and the relationship between dopamine

and cognitive performance is highly non-linear. Therefore, a patient’s response to

dopaminergic treatment is likely to vary depending on their baseline dopamine levels (Cools

and D'Esposito, 2011), and may be impaired if levels are too high. Our results show for the

first time that the response to a widely used cognitive enhancer after TBI is dependent on the

integrity of the neurotransmitter system upon which it acts. To our knowledge this is the first

clinical trial that has used an imaging measure to predict the response of TBI patients to

cognitive treatment.

We have previously shown that around one third of this group of moderate-severe TBI

patients had abnormal striatal DaT on clinical reporting of 123I-ioflupane SPECT scans and

that these patients have more cognitive impairment (Jenkins et al., 2018). Therefore, a

hypodopaminergic state is common after moderate-severe TBI and its use to guide treatment

is potentially relevant for large numbers of patients. The caudate is affected to a greater

extent than other striatal regions after TBI, although at the individual level there is a large

degree of variability in the extent to which the nigrostriatal system is disrupted. This

motivates a careful assessment of nigrostriatal damage at the individual level. In our group of

patients, DaT abnormalities in the caudate are associated with reduced substantia nigra

volume as well as evidence of nigrostriatal tract damage, particularly affecting projections to

the caudate (Jenkins et al., 2018). Taken together the neuroimaging assessment of the

nigrostriatal system after TBI shows that dopaminergic abnormalities are most commonly

seen in the caudate and that the pattern of striatal abnormality is likely to reflect the location

of nigrostriatal tract damage produced by axonal and midbrain damage.

The dopamine transporter is the main determinant of extracellular dopamine levels in the

striatum (Gainetdinov et al., 1998). Extracellular dopamine levels and neural activity regulate

its expression, with reduced DaT levels indicative of a hypodopaminergic state (Zahniser and

Doolen, 2001). The effects of methylphenidate are mediated by its blockade of the DaT and

subsequent increase in synaptic dopamine levels (Volkow et al., 2005). In addition, previous

animal models of TBI show a hypodopaminergic state after TBI that can be corrected with

the use of methylphenidate (Wagner et al., 2005; Wagner et al., 2009). We stratified the

analysis by caudate DaT levels because: a) the caudate contributes more to cognitive

processing in comparison with other striatal regions (Jahanshahi et al., 2015); b) TBI

preferentially reduces caudate DaT binding levels (Jenkins et al., 2018); and c) caudate DaT

levels relate to cognitive functioning in both healthy individuals (Mozley et al., 2001) and

Parkinson’s disease (Muller et al., 2000). Previous work has demonstrated an inverted-U

shaped relationship between higher cognitive processes such as working memory and

dopamine levels (Cools and D'Esposito, 2011). Our results support the presence of a similar

inverted-U shaped relationship for the effect of methylphenidate on a cognitive task

measuring simple response times after TBI. This has important implications for treatment.

Drugs that increase dopamine, such as methylphenidate, will shift an individual to the right

along this performance curve. The effect of this on performance will depend on the baseline

position on the curve. Hence defining an individual’s ‘dopaminergic status’ prior to making

treatment decisions is crucial, because high dopamine levels will potentially impair

performance.

One limitation of our study is that we only investigated the interaction between dopamine

status and methylphenidate treatment. Methylphenidate is a psychomotor stimulant that acts

through blockade of both dopamine and the noradrenaline transporters (Solanto, 1998),

increasing extracellular dopamine and noradrenaline levels. Hence, cognitive enhancement

might be produced by a noradrenergic mechanism. Nevertheless, we show that common

cognitive and behavioural effects can be predicted by dopamine status and we provide

evidence for a specific dopaminergic effect through the observation of an inverted U-shaped

relationship between caudate dopamine levels and methylphenidate effects. We also used the

average DaT level across both caudates for our analysis. Previous work shows a degree of

variability in the location of DaT abnormalities (Jenkins et al., 2018) and it would be

interesting to further explore whether asymmetry in DaT binding has an effect on the

response to methylphenidate treatment.

Our work shows the value of stratifying treatment selection on the basis of a biomarker that is

specific to a drugs mechanism of action. Future work could extend our proof-of-principle by

investigating interactions between drug treatment and damage to a range of neurotransmitter

systems relevant to cognitive function, including investigating both the noradrenergic system

and dopaminergic systems in the case of methylphenidate. A more comprehensive approach

might be needed to achieve optimal results, perhaps best achieved by using a range of

neuroimaging or other biomarkers to quantify the functioning of multiple neurotransmitter

systems in the same individual. This information could be used to select the most appropriate

pharmacological treatment.

Funding: This paper presents independent research funded by a National Institute of Health

Research Professorship (NIHR-RP-011-048) awarded to DJS and supported by the NIHR

Clinical Research Facility and Biomedical Research Centre at Imperial College Healthcare

NHS Trust & NIHR Clinical Research Facility. The views expressed are those of the

author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. POJ

is funded by Guarantors of Brain Clinical Fellowship.

Author contributions: POJ, SDS and DJS conceived and planned the study. PJ, SDS,

NJB, JJ and DJS collected the data. POJ, SDS, GS, AH, JHC and DJS provided the

intellectual input for statistical analysis. DT, WS, SK and MP provided imaging assessment.

POJ and DJS wrote the first draft of the manuscript. All authors reviewed and approved the

final manuscript.

Competing interests: None declared.

Figure Legends:

Figure 1. Enrolment and outcomes

Figure 2. Dopamine disruption after traumatic brain injury: A. Example 123I-ioflupane

SPECT scans. One example of a normal healthy control and four TBI patients scans showing

reduced specific binding ratios. M=Male, F=Female, time since injury for TBI patients

displayed below scans. B. Comparison of striatal DaT levels between TBI patients and

controls. Areas of significant reduction of 123I-ioflupane SBR in patients are shown in

red/yellow. The striatal mask is shown for comparison in blue. The color bar shows the

corrected p-values. C. Density plot showing the distribution of 123I-ioflupane SBR in the

caudate for healthy controls (black line). Colored points show trial patients in relation to this

distribution (red = low caudate binding i.e. >1 s.d. below the control sample mean and blue =

normal caudate binding). Dotted line represents one standard deviation below the mean SBR

for the healthy controls. D. Illustration of the ‘inverted U-shaped’ relationship between

dopamine levels and cognitive performance. Red, green and blue points represent individual

patients. Graph shows how methylphenidate treatment moves an individual along the

performance curve to the right (P=dopamine level on placebo, M=dopamine level on

methylphenidate). If there is a U-shaped relationship between dopamine level and

performance, then there will be a linear relationship between dopamine level and change in

performance ( (in this case change in response time between placebo and methylphenidate

states).

Figure 3. Response time and behavioural outcomes with methylphenidate treatment: A.

Change in the median response times on the choice reaction time task when assessed at the

study centre visits at the end of each two-week treatment period between methylphenidate

treatment and placebo. A negative value represents an improvement (i.e. a faster response

time on methylphenidate). Individual patient data are plotted in blue. B. Difference in mean

response time for the home-assessed choice reaction time tasks completed during each two-

week treatment period. C. Subjective and objectively reported apathy (LARS) and fatigue

(VAS-F) with methylphenidate compared to placebo for both the normal and low caudate

123I-ioflupane SBR groups. For both the LARS and VAS-F a negative value represents an

improvement. ** Denotes P<0·05 for Wilcoxon signed-rank test between placebo and

methylphenidate results within group. † Denotes P<0·05 for Wilcoxon rank-sum test between

the low and normal caudate 123I-ioflupane SBR groups.

Figure 4. Relationship between baseline caudate 123I-ioflupane specific binding ratio and

change in choice reaction time: Relationship between the baseline caudate 123I-ioflupane

specific binding ratio (SBR) and the change in the median response times on the choice

reaction time task when assessed at the study centre visits and also the difference in mean

response time for the home-assessed choice reaction time tasks completed during each two-

week treatment period. The baseline caudate 123I-ioflupane SBR is the age-corrected Z-score

calculated from the control group.

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Table 1. Baseline Characteristics of Patients and Control group

Characteristic

Placebo

First

(N=20)

Methylphenidate

First

(N=20)

Normal

caudate 123I-

ioflupane SBR

(N=22)

Low caudate

123I-ioflupane

SBR (N=18)

Controls

(N=20)

Age – yr 39 ± 12 40 ±12 40 ± 11 39 ± 12 40 ± 13

Male sex – no. (%) 16 (80) 18 (90) 17 (77) 17 (94) 16 (80)

Weight – kg 85 ± 13 76 ± 12 81 ± 15 79 ± 11 NA

Traumatic brain injury details

Time since injury – months 67 ± 85 83 ± 93 67 ± 86 86 ± 93 NA

Length of post-traumatic injury – days

61 ± 120 75 ± 157 37 ± 41 106 ± 197 NA

Days in hospital 48 ± 52 51 ± 54 35 ± 45 67 ± 56 NA

Lowest recorded Glasgow Coma Scale

8.3 ± 5.4 8.3 ± 5.2 9.4 ± 5.4 7.1 ± 4.8 NA

Cause of injury

RTA – no. (%) 7 (35) 14 (70) 10 (45) 11 (61) NA

Incidental Fall – no. (%) 7 (35) 1 (5) 5 (23) 3 (17) NA

Violence – no. (%) 5 (25) 4 (20) 6 (27) 3 (17) NA

Other non-intentional injury – 1 (5) 1 (5) 1 (5) 1 (6) NA

no. (%)

Functional outcome

Glasgow Outcome scale – extended

5.7 ± 0.8 5.6 ± 0.8 5.7 ± 0.8 5.4 ± 0.7 NA

Physical examination

Systolic blood pressure – mmHg 130 ± 10 123 ± 11 126 ± 12 126 ± 10 NA

Heart rate – beats/min 65 ± 10 68 ± 14 68 ± 12 65 ± 12 NA

Baseline cognitive examination

CRT median response time (ms) 416 ± 57 437 ± 59 423 ± 49 431 ± 70 378 ± 60**

CRT Intra-individual variability* 0.21 ± 0.08 0.20 ± 0.06 0.22 ± 0.08 0.18 ± 0.05 0.18 ± 0.05

Trail Making Test A (s) 30.5 ± 20.6 37.2 ± 20.7 34.9 ± 22.9 32.5 ± 18.1 19.4 ± 5.9**

Trail Making Test B (s) 69.9 ± 41.0 74.7 ± 41.0 72.4 ± 45.3 72.2 ± 35.0 45.3 ± 26.6**

Trail Making Test B-A (s) 39.4 ± 26.9 37.6 ± 23.9 37.5 ± 27.5 39.7 ± 22.6 25.9 ± 22.7

Stroop Color Naming & Word Reading Composite Score (s)

30.6 ± 7.7 30.4 ± 7.6 30.0 ± 7.2 31.1 ± 8.2 25.2 ± 5.9**

Stroop Inhibition (s) 60.7 ± 17.0 61.6 ± 14.0 60.8 ± 15.2 61.5 ± 16.0 50.9 ± 12.6**

Stroop Inhibition-Switching (s) 72.6 ± 20.0 71.8 ± 16.5 68.8 ± 18.1 76.3 ± 17.7 56.4 ± 15.7**

Stroop Inhibition-Switching vs Baseline Contrast (s)

42.2 ± 14.8 41.6 ± 10.9 39.0 ± 12.6 45.3 ± 12.6 31.6 ± 12.0**

People Test Immediate Recall 21.0 ± 8.6 24.2 ± 5.8 21.5 ± 7.6 23.8 ± 7.1 31.2 ± 3.9**

People Test Delayed Recall 8.1 ± 3.6 8.2 ± 3.1 7.7 ± 3.3 8.6 ± 3.5 10.8 ± 2.3**

People Test Forgetting 1.7 ± 2.1 2.4 ± 2.4 2.3 ± 2.2 1.7 ± 2.2 1.1 ± 2.3

WTAR Scaled 105.2 ± 14.4 109.0 ± 8.3 107.2 ± 12.7 106.9 ± 10.9 117.5 ± 5.5**

WASI Matrix Reasoning 28.3 ± 4.8 27.5 ± 3.7 27.4 ± 4.0 28.4 ± 4.5 28.6 ± 4.6

LARS Self: Total -21.9 ± 8.6 -23.2 ± 11.8 -23.7 ± 11.3 -21.0 ± 8.6 -33.4 ± 2.4**

LARS Caregiver: Total -17.8 ± 10.1 -24.0 ± 13.1 -20.3 ± 12.6 -21.0 ± 11.5 NA

Visual Analogue Scale of Fatigue 46.4 ± 17.8 42.0 ± 23.3 43.7 ± 18.0 44.8 ± 23.9 25.1 ± 19.0**

FrSBe (Self): Total (Pre) 86.5 ± 18.6 81.6 ± 16.1 79.9 ± 13.9 89.5 ± 20.2 NA

FrSBe (Self): Total (Post) 115.7 ± 30.0 113.2 ± 28.3 119.8 ± 30.7 107.6 ± 25.5 NA

FrSBe (Other): Total (Pre) 85.4 ± 16.8 83.8 ± 21.2 84.9 ± 19.6 84.2 ± 18.7 NA

FrSBe (Other): Total (Post) 109.2 ± 19.0 104.5 ± 26.7 107.4 ± 28.8 106.3 ± 14.4 NA

HADS – Anxiety 7.2 ± 4.7 8.2 ± 5.0 8.8 ± 4.9 6.2 ± 4.4 5.2 ± 4.0**

HADS – Depression 7.6 ± 5.1 6.4 ± 5.6 7.5 ± 5.0 6.4 ± 5.6 3.0 ± 2.6**

CFQ – self 48.9 ± 21.0 49.4 ± 21.1 55.1 ± 17.6 41.8 ± 22.4 31.3 ± 8.6**

CFQ – other 16.3 ± 3.9 15.8 ± 3.2 16.4 ± 3.8 15.6 ± 3.3 NA

RSAB – Other 21.1 ± 7.4 18.8 ± 11.0 21.2 ± 8.3 18.5 ± 10.4 NA

Baseline MRI characteristics

Contusions – no. (%) 14 (70) 14 (70) 13 (59) 15 (83) NA

Microhaemorrhages – no. (%) 13 (65) 13 (65) 12 (55) 14 (78) NA

Superficial siderosis – no. (%) 6 (30) 3 (15) 5 (23) 4 (22) NA

Baseline 123I-ioflupane scan

Caudate SBR 2.13 ± 0.48 2.17 ± 0.45 2.48 ± 0.28 1.75 ± 0.29 2.55 ± 0.45**

Low caudate 123I-ioflupane SBR – no. (%)

9 (50) 9 (50) NA NA NA

In this crossover study, patients were assigned to 2 weeks of placebo first with crossover to 2 weeks of methylphenidate or to 2 weeks of

methylphenidate first with crossover to 2 weeks of placebo. The patients were also split into two groups based on their caudate 123I-ioflupane

specific binding ratios. There were no significant differences between the patient groups split by drug order or caudate 123I-ioflupane specific

binding ratios (Independent T-tests were used for data distributed parametrically and the Wilcoxon rank-sum test was used for data distributed

non-parametrically). The control group and whole patient group showed significant differences on most baseline cognitive and behavioral tests.

SBR denotes Specific binding ratio, SD standard deviation, CRT Choice reaction Time task, LARS Lille Apathy Rating Scale, WTAR Wechsler

Test of Adult Reading, WASI Wechsler Abbreviated Scale for Intelligence, FrSBe Frontal Systems Behaviour Scale, HADS Hospital Anxiety

and Depression Scale, RSAB Rating Scale of Attentional Behavior, CFQ Cognitive Failures Questionnaire.

All values are the mean ± standard deviation.

* Intra-individual variability = Standard deviation of an individual’s response times/response time

** Denotes significant difference between the control group and the complete patient group (P<0.05)

Table 2. Efficacy and Safety End Points with Imaging StratificationEnd Point Normal caudate 123I-ioflupane specific binding

ratio (N=22)

Low caudate 123I-ioflupane specific binding ratio

(N=18)

Difference

between low

and normal

binding groups

(W, P value)

Placebo

Median

(IQR range)

Methylphe

nidate

Median

(IQR range)

Treatment

Difference*

Median

(95% CI)

P

Value

Placebo

Median

(IQR range)

Methylphe

nidate

Median

(IQR range)

Treatment

Difference*

Median

(95% CI)

P

Value

Efficacy

Neuropsychological

Tests

Choice Reaction Time

task median response

time - ms

357

(325–392)

362

(331–378)

1

(-10–10)0.84

382

(359–429)

369

(347–398)

-16

(-28–-3)0.02 (96, 0.049)

Choice Reaction Time

task – Intra-individual

variability**

0.18

(0.14–0.22)

0.18

(0.14–0.22)

0.00

(-0.04–0.03)0.71

0.17

(0.15–0.21)

0.16

(0.15–0.19)

-0.01

(-0.03–0.02)0.51 (126, 0.49)

Trail Making Test A (s) 21.0

(19.0–30.8)

22.0

(16.3–34.0)

-1.0

(-3.5–4.0)0.84

25.0

(16.8–34.0)

25.0

(20.3–33.8)

0.0

(-5.0–5.0)1 (174, 0.91)

Trail Making Test B (s) 49.0

(37.8–69.0)

55.0

(35.3–81.3)

3.0

(-8.0–11.5)0.58

48.0

(42.5–68.3)

54.0

(50.3–65.8)

10.0

(-5.0–16.5)0.28 (205, 0.62)

Trail Making Test B-A

(s)

22.0

(16.3–38.5)

23.0

(16.0–46.0)

2.0

(-8.5–8.5)0.95

31.0

(25.3–32.8)

28.0

(25.3–41.5)

4.0

(-5.0–17.0)0.28 (211.5, 0.34)

Stroop Color Naming &

Word Reading

Composite Score (s)

27.8

(24.8–31.4)

28.8

(24.6–20.0)

0.0

(-2.8–1.3)0.43

28.5

(25.0–33.6)

29.0

(25.1–31.4)

0.5

(-1.8–1.8)0.92 (208, 0.56)

Stroop Inhibition (s) 55.5

(44.5–59.0)

57.5

(47.3–62.0)

1.0

(-4.0–4.0)0.97

55.0

(44.3–61.0)

54.5

(43.3–67.8)

-4.5

(-6.5–3.5)0.34 (177.5, 0.58)

Stroop Inhibition-

Switching (s)

59.0

(51.3–71.8)

63.0

(52.3–67.8)

2.0

(-6.0–2.0)0.40

69.5

(60.0–75.3)

65.0

(52.0–79.5)

-3.0

(-9.5–2.0)0.13 (166, 0.56)

Stroop Inhibition-

Switching vs Baseline

Contrast (s)

31.5

(26.0–42.8)

31.5

(25.3–39.3)

-1.5

(-5.5–2.5)0.43

40.0

(32.5–45.8)

33.5

(25.0–50.5)

-4.0

(-10.0–1.5)0.12 (162, 0.33)

People Test Immediate

Recall

32.0

(28.3–35.5)

32.0

(27.3–33.8)

0.0

(-4.0–4.0)0.87

34.0

(30.0–36.0)

34.0

(29.0–36.0)

0.0

(-5.0–9.0)0.85 (182, 0.89)

People Test Delayed

Recall

12.0

(10.0–12.0)

11.0

(9.0–12.0)

0.0

(-3.5–2.0)0.59

12.0

(12.0–12.0)

12.0

(10.0–12.0)

0.0

(-4.5–1.5)0.21 (196.5, 0.78)

People Test Forgetting 0.0

(0.0–2.0)

0.0

(0.0–2.0)

0.0

(-2.5–3.0)0.87

0.0

(0.0–0.0)

0.0

(0.0–2.0)

0.0

(-4.0–2.0)0.18 (218.5, 0.35)

WASI Matrix

Reasoning***

29.0

(28.0–31.0)

28.5

(26.0–31.5)

0.0

(-2.0–1.0)0.48

30.0

(27.0–32.0)

30.0

(26.0–32.0)

1.0

(-1.5–1.5)0.71 (159, 0.43)

Functional outcome

Glasgow Outcome scale

– extended

6.0

(5.0–6.0)

6.0

(5.0–6.0)

0.0

(-1–1)1

5.0

(5.0–6.0)

6.0

(5.0–6.0)

0.0

(0.0–1.0)0.06 (144, 0.08)

Behavioral

Questionnaires

Lille Apathy Rating

Scale Self: Total

-26.5

(-29.8–-

15.3)

-29.5

(-30.8–-

20.5)

-1.0

(-6.5–0.5)0.07

-23.0

(-28.0–-

17.0)

-29.0

(-31.0–-

24.0)

-2.0

(-9.0–0.0)0.03 (154.5, 0.36)

Lille Apathy Rating

Scale Caregiver: Total

-25.5

(-29.5–-

10.0)

-27.0

(-30–-12)

-0.5

(-10.0–7.5)0.98

-21.0

(-26.8–16.8)

-27.5

(-33.0–18.5)

-3.5

(-7.0–0.0)0.02 (48.5, 0.18)

Visual Analogue Scale

for Fatigue

56.1

(28.7–63.6)

34.5

(23.1–46.8)

-6.6

(-18.6–-0.7)0.03

45.8

(36.8–54.8)

24.5

(15.1–38.3)

-7.5

(-23.4–-3.2)0.007 (161, 0.61)

Frontal Systems

Behaviour Scale (Self):

Total (Now)

107.0

(96.3–

134.0)

109.8

(91.3–

141.0)

-5.0

(-7.5–1.0)0.08

102.0

(85.0–

106.0)

94.0

(83.0–

107.0)

-3.0

(-9.0–4.0)0.44 (201, 0.70)

Frontal Systems

Behaviour Scale

(Other): Total (Now)

107.5

(80.3–

124.3)

98.0

(77.8–

108.3)

-0.5

(-26.5–3.5)0.47

99.0

(94.0–

111.5)

92.0

(86.8–

101.5)

-8.5

(-11.0–6.0)0.27 (69.5, 0.47)

Hospital Anxiety and

Depression Scale –

Anxiety

7.0

(4.0–11.8)

6.0

(3.0–14.0)

1.0

(-2.0–1.5)0.75

4.0

(1.0–6.0)

3.0

(2.0–6.0)

-1.0

(-2.0–1.5)0.48 (164, 0.67)

Hospital Anxiety and

Depression Scale –

Depression

5.0

(4.0–11.5)

6.0

(3.0–9.0)

-1.0

(-2.5–1.0)0.55

3.0

(2.0–8.0)

3.0

(1.0–6.0)

-1.0

(-6.0–2.0)0.28 (154, 0.47)

Cognitive Failures 50.5 48.0 -2.0 0.50 30.0 30.0 0.0 0.92 (203.5, 0.65)

Questionnaire – self (37.5–63.8) (34.5–60.8) (-9.0–5.5) (22.0–51.0) (22.0–45.0) (-7.0–5.0)

Cognitive Failures

Questionnaire – other

16.0

(14.0–18.0)

14.0

(9.0–19.0)

0.0

(-6.5–2.0)0.41

14.0

(12.0–19.0)

14.0

(9.0–16.0)

-3.0

(-6.0–1.0)0.11 (77.5, 0.73)

Rating Scale of

Attentional Behavior –

Other

16.0

(9.0–24.0)

15.5

(12.8–18.5)

1.0

(-15.0–7.5)1

13.0

(8.0–18.0)

12.0

(8.8–15.8)

-2.0

(-6.0–2.5)0.28 (58.5, 0.45)

Physical examination

Systolic blood pressure

– mmHg

122

(115–138)

132

(118–138)

2

(-3–10)0.28

123

(114–131)

127

(121–131)

2

(-5–9)0.51 (213, 0.69)

Heart rate – beats/min 63

(58–76)

71

(66–84)

6

(3–15)0.002

60

(55–68)

67

(62–77)

6

(3–11)0.008 (206.5, 0.82)

The Wilcoxon signed-rank test was used to compare the within group difference between drug and placebo states. The Wilcoxon rank-sum test

was used to compare the treatment effects between the low and normal caudate dopamine transporter groups.

* The treatment difference is the value in the methylphenidate group minus the value in the placebo group (measures where a lower value and

therefore a negative treatment difference is an improvement are the Choice Reaction time, Trail Making Test, Stroop Test, People Test

Forgetting, Lille Apathy Rating scale, Visual Analogue Scale for Fatigue, Frontal Systems Behaviour Scale, Hospital Anxiety and Depression

Scale, Cognitive Failures Questionnaire, and Rating Scale of Attentional Behaviour). The median difference and 95% confidence interval were

calculated with the use of the Hodges-Lehmann approach.

** Intra-individual variability = Standard deviation of an individual’s response times/response time

*** WASI: Wechsler Abbreviated Scale for Intelligence

Figure 1

Figure 2

Figure 3

Figure 4