George Bello

Bertha Simwaka

Tchaka Ndhlovu

Felix Salaniponi

Timothy B Hallett
email: timothy.hallett@imperial.ac.uk

Evidence for Changes in Behaviour Leading to Reductions in HIV Prevalence in Urban Malawi

Keywords: HIV, Epidemiology, Prevention, Mathematical modelling 2

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Introduction

Almost thirty years into the HIV epidemic, there is more need than ever to understand how interventions can stop the spread of HIV. Following billion dollar investments in prevention interventions by PEPFAR and the Global Fund, there remains little sign of epidemics coming under control [START_REF] Sepulveda | Committee for the Evaluation of the President's Emergency Plan for AIDS Relief (PEPFAR) Implementation[END_REF], weakening the case for renewed investment. Indeed, the accumulation of randomised-control trials that failed to show efficacy [START_REF] Weiss | Persisting with prevention: The importance of adherence for HIV prevention[END_REF], coupled with most large HIV epidemics seeming to maintain steady endemic states [START_REF]Report on the global AIDS epidemic[END_REF], has led to 'crisis of confidence' in an ability to prevent infections through changes in behaviour. This vacuum of compelling prevention causes has been filled with optimistic projections for the impact of male circumcision interventions [START_REF] Unaids/Who/ | SACEMA Expert Group on Modelling the Impact Cost of Male Circumcision for HIV Prevention. Male Circumcision for HIV Prevention in High HIV Prevalence Settings: What Can Mathematical Modelling Contribute to Informed Decision Making?[END_REF][START_REF] Hallett | Understanding the impact of male circumcision interventions on the spread of HIV in southern Africa[END_REF], pre-exposure prophylaxis [6] and the suggestion that immediate initiation of anti-retroviral therapy would be a more effective way to reduce HIV transmission [START_REF] Granich | Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model[END_REF] -but for HIV to be eliminated, not just reduced, these interventions would always have to be reinforced with substantial changes in behaviour.

However, arguments about the lack of success in HIV prevention through behaviour change have over-looked routine HIV surveillance data as a key source of information about the success of interventions and whether behaviour change can affect the course of large matured generalised epidemics. Indeed, it was through analyses of routine epidemiological surveillance data that the success of the 'zero-grazing' campaign in Uganda [START_REF] Kilian | Reductions in risk behaviour provide the most consistent explanation for declining HIV-1 prevalence in Uganda[END_REF][START_REF] Stoneburner | Population-level HIV declines and behavioral risk avoidance in Uganda[END_REF], and the '100% condom' campaign in Thailand [START_REF] Nelson | Changes in sexual behavior and a decline in HIV infection among young men in Thailand[END_REF][START_REF] Rojanapithayakorn | The 100% condom program in Thailand[END_REF] were first detected; and more recently, where evidence for dramatic reductions in incidence in Zimbabwe was found [START_REF] Unaids | Evidence for HIV decline in Zimbabwe: a comprehensive review of the epidemiological data[END_REF][START_REF] Hallett | Assessing evidence for behaviour change affecting the course of HIV epidemics: A new mathematical modelling approach and application to data from[END_REF]. However, such analyses are not straightforward for two reasons: first, when epidemics mature, prevalence can decline even if individuals do not change their risk behaviour [START_REF] Kilian | Reductions in risk behaviour provide the most consistent explanation for declining HIV-1 prevalence in Uganda[END_REF][START_REF] Hallett | Declines in HIV prevalence can be associated with changing sexual behaviour in Uganda, urban Kenya, Zimbabwe, and urban Haiti[END_REF][START_REF] Walker | Interpreting declines in HIV prevalence: The impact of spatial aggregation and migration on expected declines in prevalence[END_REF] and these 'natural epidemiological dynamics' confound simple statistical tests for trends in prevalence (since a significant decline in prevalence does not necessarily indicate behaviour changes reducing infectious spread). Second, the non-linear threshold relationship between sexual risk behaviour and HIV spread means changes in sexual behaviour indicators, though apparently substantial, may not be epidemiologically relevant: for instance, if they are overwhelmed by high degrees of risk of other types, or they do not reduce the level of risk below a certain threshold, or they are confined to parts of the population at little risk of acquiring of transmitting infection [START_REF] Grassly | The effectiveness of HIV prevention and the epidemiological context[END_REF][START_REF] Hallett | Appropriate evaluation of HIV prevention interventions: from experiment to full-scale implementation[END_REF].

Therefore, to interpret the information contained in these data it is essential to construct a mathematical model that provides a mechanistic description of the link between individual sexual behaviour, transmission in the population and that can simulate these natural epidemiological dynamics. Observed trends in prevalence data can then be compared against the model output to assess the evidence for observed changes in sexual behaviour actually having an impact on the course of the epidemic, beyond what natural changes in prevalence would be expected.

In this paper we present an analysis of the epidemic in Malawi and show evidence that changes in number of sex partners and condom use have contributed to a large reduction in HIV prevalence in urban areas.

Methods

Our analysis was conducted in two stages: first we analysed trends in key indicators of sexual behaviour over time; and second, we used a mathematical model to test for the association between these trends and the observed trajectory of the HIV epidemic.

Analysis of Trends in Sexual Behaviour Indicators

The best available data on sexual behaviour among the general population comes from the Malawi Demographic Health Surveys. Two surveys have been completed in Malawi -in 2000 (N=13,220 women and 3,092 men) and 2004 (N=11,698 women and 3,261 men) [START_REF]Macro O. Malawi Demographic and Health Survey[END_REF][START_REF]Macro O. Malawi Demographic and Health Survey[END_REF].

These use a nationally-representative sample of households, and administer a face-to-face survey that includes questions on key sexual behaviour indicators.

These data were used to construct the following indicators for both surveys, which correspond to the three dimensions of sexual behaviour that are commonly associated with HIV risk -early sexual debut, partner numbers, and condom use:

Fraction of men/women aged 15-19 that first had sex before aged 15 years.

Fraction of men/women aged 15-24/15-49 that have had sex with two or more partners in the last 12 months.

Fraction of men/women aged 15-25/15-49 that have had sex with two or more partners in the last 12 months that used a condom at last sex.

Mathematical Modelling Analysis

A mathematical model of the HIV epidemic in Malawi was parameterised with behavioural indicator data and compared to prevalence data in a Bayesian melding framework [START_REF] Poole | Inference for deterministic simulation models: The Bayesian melding approach[END_REF][START_REF] Alkema | Probabilistic projections of HIV prevalence using Bayesian melding[END_REF] to test the hypothesis that the observed trends in prevalence partly result from observed changes in sexual behaviour.

The mathematical model represents the heterosexual transmission of HIV in a sex and sexual-activity stratified sexually-active adult population (without detailed age structure) [START_REF] Hallett | Declines in HIV prevalence can be associated with changing sexual behaviour in Uganda, urban Kenya, Zimbabwe, and urban Haiti[END_REF][START_REF] Garnett | Monitoring the course of the HIV-1 epidemic: the influence of patterns of fertility on HIV-1 prevalence estimates[END_REF], and is defined by a set of ordinary differential equations that are solved numerically. Full details of the model have previously been published by Hallett et al. [START_REF] Hallett | Assessing evidence for behaviour change affecting the course of HIV epidemics: A new mathematical modelling approach and application to data from[END_REF],

and further information is available from the authors on request.

For each model parameter, a probability distribution is defined that represents prior knowledge about likely values. Where possible, these priors are based on survey data but if there is no information for a particular parameter a non-informative prior is used. Details about the main parameters are listed in Table S1 in the online material. Prior limits on simulated prevalence can also be set so that, despite there being little data available early in the epidemic, simulations in which prevalence rise too quickly or slowly can be excluded [START_REF] Alkema | Probabilistic projections of HIV prevalence using Bayesian melding[END_REF][START_REF] Alkema | Bayesian melding for estimating uncertainty in national HIV prevalence estimates[END_REF].

The simulated prevalence time-series is compared to prevalence data from the national Ante-natal Clinic (ANC) surveillance system. In Malawi, this system was started in 1994 and has included 28 different clinics in urban area and 26 different clinics in rural areas.

In total, there are data on 70,922 women (53,556 in urban settings, 17,366 in rural settings).

However, many of these new sites were added only in 2007, and the number of sites consistently sampled between 1994 and 2007 is 11 in urban areas and 8 in rural areas. Note that these are the 'raw' data collected in the country and the changes in methods used by UNAIDS to produce their national prevalence estimates would not interfere with this analysis [START_REF] Ghys | Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections[END_REF].

The model is compared to these data using an approach that analyses trends in prevalence within each site ('random effects model'), to avoid the possibility that declines in prevalence are spuriously indicated by the recruitment of progressively more remote clinics in the sentinel surveillance system [START_REF] Brown | Improving projections at the country level: the UNAIDS Estimation and Projection Package 2005[END_REF]. Since estimates of prevalence in the ANC data may include a systematic bias with respect to prevalence in the general population [START_REF] Garnett | Monitoring the course of the HIV-1 epidemic: the influence of patterns of fertility on HIV-1 prevalence estimates[END_REF][START_REF] Zaba | Monitoring the AIDS epidemic using HIV prevalence data among young women attending antenatal clinics: prospects and problems[END_REF], the model-simulated prevalence time-series is recalibrated to correct for any observed discrepancy between ANC measures of prevalence and measures of prevalence in the general population in the same year [START_REF] Alkema | Bayesian melding for estimating uncertainty in national HIV prevalence estimates[END_REF][START_REF] Gouws | Comparison of HIV prevalence in population-based national surveys and ANC surveillance: Implications for calibrating surveillance data[END_REF].

To test the hypothesis that changes in sexual behaviour have contributed to altering the natural course of the epidemic, two models are compared: one which does not allow behaviour change (M 0 ), with one that does (M 1 ). The Bayes factor measures the relative agreement to the data of the two models (M 1 versus M 0 ) across all parameter space: [30, 31], we judge that there is strong evidence for behaviour change influencing the epidemic if

( ) ( ) ( ) ( ) ∫ ∫ = θ θ W θ θ θ W θ d M p p d M p p K
( ) K ln 2
>5, indicating that model M 1 is in substantially better agreement with the data. In that case, the number of infections averted is calculated by comparing two runs of model M 1 , with the behaviour change parameters either set to their best-fitting values or to values indicating no behaviour change.

Results

Trends in Sexual Risk Behaviour Indicators

Trends in the key sexual behaviour indicators are summarised in Figure 1. The percentage of young women and men aged 15-19 who first had sex before the age of 15 decreased in both sexes between 2000 and 2004 (Figure 1(a)). In young men, the decrease was more substantial (reducing from 29% in 2000 to 18% in 2004 (p<0.001)) where as in young women the change was modest (reducing from 17% in 2000 to 14% in 2004 (p=0.01)).

In the same period, there was substantial decrease in proportion of men who had sex with more than two or more partners in the last 12 months: reducing from 15% in 2000 to 9% among adult men aged 15-49 years (p<0.001), and from 12% in 2000 to 7% in 2004 among 15-24 year-olds (p<0.001) (Figure 1(b)). Only 1% of women reported having two or more partners and this did not change between the two surveys.

The proportion of men with two or more partners that used condoms increased between 2000 and 2004: from 14% in 2000 to 20% in 2004 among 15-49 year-old men (p=0.04), and -not statistically significantly -from 27% in 2000 to 35% in 2004 among 15-24 year-old men (p=0.2). There was no change in the proportion of women reporting using condoms, but the number of women on which these estimates were based is small (<200?).

Association between Behaviour Change and Changes in HIV Prevalence

Between 1994 and 2007, 11 urban ANC sites were sampled consistently. Among these, mean and median HIV prevalence peaked in 1999, at 26%, and has declined since: in 2007 mean prevalence in the same sites was 15% (Figure 2(a)). Between 1999 and 2005, prevalence in all sites declined, and by an average amount of 30%. In rural areas, 8 sites were consistently included in the surveillance system. Among these, there is a less clear trend in mean and median HIV prevalence over time (Figure 2(b)). Between 1999 and 2005, 4 sites indicated a decline in prevalence, and 4 sites indicated an increase. Among 15-24 year-old women attending ANC, prevalence declined in all urban and semi-urban sites, and by an average amount of 40% (Figure 2(c)). In most rural sites there was little change in prevalence (average change across sites, excluding Kasina , was -2%).

The model was used to test the hypothesis that the observed trends in prevalence partly result from the observed changes in sexual behaviour. In urban areas, the fit of model M 1 (with behaviour change) was superior to the fit of model M 0 without behaviour change (p-value of likelihood ratio test << 0.001). In addition, accounting for the information from the behavioural data in the Bayes factor analysis, there was strong evidence for behaviour change affecting the course of the epidemic (2log(K) = 19). Figure 3(a) shows the posterior distribution for prevalence and Figure 3(b) show the posterior distribution for incidence model M 1 , with a clear inflection in the period 2000-2002 where the impact of changes in behaviour takes effect. Table S1 shows updated distribution for the key parameters in the analysis.

In rural areas, there was little decline in HIV prevalence and there was no evidence for behaviour changes affecting the course of epidemic in rural areas (likelihood ratio test, pvalue = 0.49; 2log(K) ≈ 0). Figure 3(c) and Figure 3(d) shows the posterior distribution prevalence and incidence, respectively, for model M 0 . Prevalence and incidence are approximately constant in the period 1999-2005, although a modest downturn in incidence is weakly supported by the data.

The number of HIV infections averted in urban areas was calculated by removing the effects of behaviour change in the posterior fits of model M 1 . By 2004, approximately 42,000 infections were averted (95% interval: 17,000-57,000). Assuming that the changes in behaviour were maintained, then in total 140,000 (95% interval: 65,000 -160,000) infections will have been averted by 2010.

Discussion

We have presented evidence that the recent downturn in HIV prevalence in urban Malawi has partly resulted from the concomitantly observed changes in sexual risk behaviour, whilst the epidemic in rural Malawi has remained stable. The risk changes began in 2000-2002, and have averted ~140,000 HIV infections by 2010, and this will lead to reduced AIDS death and ART costs in the future.

The proximate factors most consistent with underpinning this reduction in risk include reductions in proportion of men that have several sexual partners, and an increase in condom use. The changes in the timing of starting sex is less likely to have played a substantial role in reducing risk overall, since these changes probably lead to only modest reductions in the lifetime and population-level risk of HIV infection [START_REF] Hallett | Behaviour change in generalised HIV epidemics: impact of reducing cross-generational sex and delaying age at sexual debut[END_REF]. Recognising that the impact of changes in proximate determinants of risk can be different according to the precise nature of the epidemic context, about which we have collated limited information, it seems that the reductions in the number of men with multiple partners is more likely to have played the principal role, because these changes were more substantial than the changes in condom use and, in mathematical models, large reduction in partner numbers tend to influence transmission more than small increases in condom use [START_REF] Garnett | Fewer partners or more condoms? Modelling the effectiveness of STI prevention interventions[END_REF]. However, an important caveat is that only basic indicator information about sexual behaviour was available and other aspects of sexual behaviour not directly recorded (e.g. concurrency of partnerships [START_REF] Morris | Concurrent partnerships and the spread of HIV[END_REF], consistency of condom use across many sex acts [START_REF] Bracher | Assessing the potential of condom use to prevent the spread of HIV: a microsimulation study[END_REF]) , which can also have an important influence of HIV transmission, could also have changed and formed a part of the difference in overall risk.

What precipitated these behaviour changes at around 2000-2002? There are two main possibilities. The 2006 Malawi Triangulation Project [START_REF]Malawi Triangulation Project:Synthesis of Data on Trends in the National and Local HIV Epidemics And The Reach and Intensity of Prevention Efforts. Process, Key Findings, and Recommendations[END_REF], reviewed published and unpublished data on the expansion of intervention programmes, and found a broad correlation between the reach of interventions in regions and a downward trend in risk behaviour and HIV prevalence. That reported credited increases in condom distribution and HIV testing with a primary role in driving the change in HIV incidence. However, whilst programmes may have equipped individuals with the information and services they needed to reduce their risk of infection, they may not necessarily have provided the impetus for behaviour change. In Malawi in the early 2000's, witnessing AIDS illnesses and deaths formed a part of everyday life for most people, and it seems likely that the fear of becoming infected could motivate substantial changes in sexual behaviour [START_REF] Mckusick | Reported changes in the sexual behavior of men at risk for AIDS, San Francisco, 1982-84--the AIDS Behavioral Research Project[END_REF] (The "Funeral Effect").

The risk reduction in Zimbabwe and Uganda have at least partly been attributed to this process [START_REF] Stoneburner | Population-level HIV declines and behavioral risk avoidance in Uganda[END_REF][START_REF] Unaids | Evidence for HIV decline in Zimbabwe: a comprehensive review of the epidemiological data[END_REF]. Although the model indicates that risk reduced in the period 2000-2002, this is a simplification and it is more likely the a gradual change in behaviour beginning in the late 1990s gathered pace or reach a 'tipping point' around 2000-2002 when it began to have an influence on the epidemic.

In this analysis we have focussed on selection of three key behaviour indicators that do not fully capture the range of ways that sex can lead to infection risk. The data are also collected in a face-to-survey and there is scope for social desirability biases to both understate the level of sexual risk behaviour and to generate a trend towards reporting safer sexual behaviour over time [START_REF] Gregson | Methods to reduce social desirability bias in sex surveys in low-development settings: experience in Zimbabwe[END_REF]. These finding from the model are based on accepting that the model adequately reflects HIV transmission in these populations. In particular, the erroneous conclusion that behaviour change has contributed to prevalence declines would be drawn if the simulation model does not fully capture the extent to which prevalence can decline as a natural part of the epidemic maturation. The model does not explicitly represent how the sample of women attending ANC might change as the epidemic evolves and AIDS-related sub-fertility leads to more infected women being excluded from the ANC sample. This could theoretically lead to greater declines in prevalence, but models that have examined this [START_REF] Hallett | Declines in HIV prevalence can be associated with changing sexual behaviour in Uganda, urban Kenya, Zimbabwe, and urban Haiti[END_REF][START_REF] Garnett | Monitoring the course of the HIV-1 epidemic: the influence of patterns of fertility on HIV-1 prevalence estimates[END_REF][START_REF] Clark | An investigation into the impact of HIV on population dynamics in Africa[END_REF] find that any such change is likely to occur shortly after incidence peaks, so later declines in prevalence cannot be explained by this process.

The model also does not capture the finite duration of sexual partnerships, temporal overlaps between sexual partnerships [START_REF] Morris | Concurrent partnerships and the spread of HIV[END_REF] or the possible range of sex partner network structures that have been shown to influence patterns of transmission [START_REF] Ghani | Risks of acquiring and transmitting sexually transmitted diseases in sexual partner networks[END_REF]. For instance, the model used here could under-estimate declines in prevalence if most transmission in the early phase of the epidemic is associated with a large network component supported by only a few individuals. However, models that do capture these effects tend to show much less substantial declines in prevalence than this model generates. In fact, the range of parameter values supported by the prior distribution in this model is likely to generate greater declines than could realistically be expected, and will therefore lead to conservative conclusions being drawn about the influence of behaviour change.

In conclusion, we have shown that changes in sexual behaviour can avert thousands of new HIV infections in mature generalised hyper-endemic settings. In urban Malawi, the reduction in the number of men with multiple partners is the factor most consistent with driving the reduction in incidence. However, understanding the causes of this change is a priority, so that successful programmes and campaigns can be rapidly expanded to rural areas and other countries in the region.

W

  .....................................................................…. (1) Here θ denotes the model input parameters, ( ) θ p is the prior probability of that parameter set, and is the likelihood of the simulated epidemic given the data from ante-natal clinics in the model M x . Following convention

Figure 1 :

 1 Figure 1: Changes in key sexual behaviour indicators between 2000 and 2004. Source: Malawi Demographic and Health Surveys in 2000 and 2004. The star indicates a statistically significant difference (p<0.05).

Figure 2 :

 2 Figure 2: Trends in prevalence among consistent ANC sites in (a) Urban, and (b) Rural Malawi; and (c) Among women aged 15-24 year-olds. In a and b, the diamonds and blue line show the median prevalence across sites; the squares and red line show the weighted mean prevalence across sites. In c, each bar shows the percentage change in prevalence in that ANC site between 1999 and 2005 (negative values indicate reductions).Source: Malawi national ANC surveillance system.

Figure 3 :

 3 Figure 3: Bayesian Model fits to the Malawi epidemic in urban and rural areas of Malawi. Panels (a) and (c) show the modelled HIV prevalence (%) trend over time: the red crosses show data points from individual ANC sites, and the red lines join data from the same clinic; the solid line show the modal model (with calibration) run in the posterior distribution (ie. the run best supported jointly by the prevalence and the behaviour data) and the dashed lines show 95 % interval for prevalence at each point in time from the posterior distribution. Panels (b) and (d) show the modelled HIV incidence (per 100 person years at risk) trend over time for the general population: the solid line shows the median incidence estimate and the dashed lines show the 95% interval for incidence at each point time from the posterior distribution. The model fit shown for the urban areas in Model M 1 , and the model fit shown for rural areas is M 0 .

  d

Acknowledgements

TBH thanks The Wellcome Trust for funding support.

Author Contributions

All authors contributed to designing and analysing/interpreting the model and writing the manuscript. GB lead the compilation of data and TBH lead the modelling component.

Competing Interests

None declared


Andre Frank Marquand
email: andre.marquand@kcl.ac.uk

MSc Sara De Simoni

MSc Owen G O'daly

PhD Steven C R Williams

Janaina Mourao-Miranda

PhD Mitul A Mehta

PhD Janaina Mourão-Miranda

Andre Marquand

Pattern classification of working memory networks reveals differential effects of methylphenidate, atomoxetine and placebo in healthy volunteers

Keywords: Methylphenidate, Atomoxetine, Working memory, Reward, Pattern recognition

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Stimulant and non-stimulant medications that influence dopamine (DA) and noradrenaline (NA) neurotransmission can reduce symptoms of attention deficit/hyperactivity disorder (ADHD). The stimulant drug methylphenidate (MPH) has been shown to have consistently greater clinical efficacy than atomoxetine (ATX), a non-stimulant drug recently approved for the treatment of ADHD in the USA and Europe [START_REF] Spencer | Effectiveness and tolerability of tomoxetine in adults with attention deficit hyperactivity disorder[END_REF][START_REF] Michelson | Atomoxetine in the treatment of children and adolescents with attentiondeficit/hyperactivity disorder: A randomized, placebo-controlled, dose-response study[END_REF][START_REF] Faraone | Efficacy of atomoxetine in adult attention-deficit/hyperactivity disorder: a drug-placebo response curve analysis[END_REF][START_REF] Kemner | Outcomes of OROS (R) methylphenidate compared with atomoxetine in children with ADHD: A multicenter, randomized prospective study[END_REF][START_REF] Starr | Multicenter, randomized, open-label study of OROS methylphenidate versus atomoxetine: Treatment outcomes in African-American children with ADHD[END_REF][START_REF] Newcorn | Atomoxetine and osmotically released methylphenidate for the treatment of attention deficit hyperactivity disorder: Acute comparison and differential response[END_REF]. ATX nonetheless offers several potential advantages over MPH including reduced abuse liability, reduced risk of motor side effects and as an alternative treatment for patients non-responsive to stimulants [START_REF] Newcorn | Atomoxetine and osmotically released methylphenidate for the treatment of attention deficit hyperactivity disorder: Acute comparison and differential response[END_REF]. However, the mechanisms underlying their differences on human brain function are unclear.

There is converging evidence that weakened PFC function underlies several of the hallmark deficits in ADHD [START_REF] Arnsten | Stimulants: Therapeutic actions in ADHD[END_REF]. In particular, working memory (WM) -the ability to hold and manipulate information for future action -is impaired in ADHD [START_REF] Martinussen | A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder[END_REF][START_REF] Willcutt | Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review[END_REF] and has been strongly linked to the activity of the catcholamines (DA and NA) within the PFC [START_REF] Brozoski | Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus-monkey[END_REF][START_REF] Arnsten | Alpha-2-adrenergic mechanisms in prefrontal cortex associated with cognitive decline in aged non-human primates[END_REF]. WM performance is also known to be improved with MPH [START_REF] Elliott | Effects of methylphenidate on spatial working memory and planning in healthy young adults[END_REF][START_REF] Bedard | Methylphenidate improves visual-spatial memory in children with attention-deficit/hyperactivity disorder[END_REF][START_REF] Mehta | Methylphenidate improves working memory and setshifting in AD/HD: relationships to baseline memory capacity[END_REF], currently understood as resulting from an increased efficiency of fronto-parietal WM regions demonstrated using PET neuroimaging studies [START_REF] Mehta | Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human brain[END_REF][START_REF] Schweitzer | Effect of methylphenidate on executive functioning in adults with attentiondeficit/hyperactivity disorder: Normalization of behavior but not related brain activity[END_REF]. Studies in experimental animals suggest ATX has a similar ability to improve WM function [START_REF] Gamo | methylphenidate and atomoxetine enhance prefrontal function through alpha-2 adrenergic and dopamine D1 receptors[END_REF], via effects on prefrontal cortical activity, although there are no comparative human neuroimaging studies of the effects of MPH and ATX on WM networks.

Previous studies in experimental animals have indicated that: (1) MPH inhibits both DA and NA transporters (DAT and NAT respectively; [START_REF] Seeman | Anti-hyperactivity medication: methylphenidate and amphetamine[END_REF][START_REF] Han | Comparison of the monoamine transporters from human and mouse in their sensitivities to psychostimulant drugs[END_REF], (2) ATX is a selective inhibitor of NAT [START_REF] Wong | A new inhibitor of norepinephrine uptake devoid of affinity for receptors in rat-brain[END_REF][START_REF] Bolden-Watson | Blockade by newly-developed antidepressants of biogenicamine uptake into rat-brain synaptosomes[END_REF] and (3) both drugs increase concentrations of DA and NA in the prefrontal cortex (PFC), but only MPH increases DA in the striatum [START_REF] Bymaster | Atomoxetine increases extracellular levels of norepinephrine and dopamine in prefrontal cortex of rat: A potential mechanism for efficacy in Attention Deficit/Hyperactivity Disorder[END_REF]. However, the neural consequences of these differential actions in humans and their implications for functional brain networks are currently unknown.

Theoretically, systemically administered MPH and ATX may differentially influence distributed brain regions due to localised effects at DAT and NAT sites [START_REF] Ciliax | Immunocytochemical localization of the dopamine transporter in human brain[END_REF][START_REF] Schou | Post-mortem human brain autoradiography of the norepinephrine transporter using (S,S)-F-18 FMeNER-D-2[END_REF] and consequent effects on connected brain areas, in addition to the differential effects on striatal catecholamine neurotransmission demonstrated in rodents [START_REF] Bymaster | Atomoxetine increases extracellular levels of norepinephrine and dopamine in prefrontal cortex of rat: A potential mechanism for efficacy in Attention Deficit/Hyperactivity Disorder[END_REF]. Thus, differential effects of MPH and ATX may be distributed across multiple brain regions. Multivariate pattern recognition (PR) methods are sensitive to such spatially distributed information by making use of the correlation between brain voxels and afford substantially greater sensitivity than conventional massunivariate analysis methods [START_REF] Haynes | Decoding mental states from brain activity in humans[END_REF][START_REF] Kriegeskorte | Information-based functional brain mapping[END_REF][START_REF] Norman | Beyond mind-reading: multi-voxel pattern analysis of fMRI data[END_REF].

Therefore, we combined event-related fMRI with a novel whole-brain PR analytic approach in order to characterise and discriminate acute effects of MPH and ATX in healthy volunteers performing a WM task. While we expected reductions in PFC activity after MPH, this study represents the first attempt to: (1) examine the effects of ATX on WM networks and (2) test potential differences between prefrontal cortical and striatal activation following administration of MPH and ATX in humans.

Finally, recent literature suggests an important contribution of reward to the regulation of WMrelated brain activity [START_REF] Ichihara-Takeda | Reward acts as a signal to control delay-period activity in delayed-response tasks[END_REF]. This accords with evidence that both reward and MPH have similar effects on sustained attention task performance in ADHD [START_REF] Trommer | The go-no go test in attention-deficit disorder is sensitive to methylphenidate[END_REF][START_REF] Andreou | Reaction time performance in ADHD: improvement under fastincentive condition and familial effects[END_REF]. Therefore, we also explored the role of reward on WM function, with a focus on determining its impact on our ability to discriminate MPH and ATX.

Methods

Participant recruitment and data acquisition

Fifteen healthy male university students and members of the general public (aged 20-39) were recruited by local advertisement and were scanned on three occasions. Participants were screened by interview and physical exam for previous or current medical, psychiatric or neurological problems. Other exclusion criteria included any substance abuse history, smoking >5 cigarettes per day and consuming the equivalent of >5 cups of coffee per day. Participants were trained on the WM task on the screening day and were asked to refrain from alcohol and caffeine containing products for 24 hours prior to dosing. Participants provided written consent and the study was approved by South London Research Ethics Committee 3. On each scanning day, participants were screened for drugs of abuse and alcohol, then each participant received an oral dose of MPH (30mg), ATX (60mg), or a placebo (PLC) according to a randomised, double-blind Latin square design. Doses of MPH and ATX were chosen to approximately match doses used in clinical practice, and doses reported in the literature (e.g. [START_REF] Gilbert | Comparison of the inhibitory and excitatory effects of ADHD medications methylphenidate and atomoxetine on motor cortex[END_REF].

Scanning was performed on a on a General Electric Signa HDx 3T scanner and was timed to coincide with the peak plasma concentration for MPH and ATX [START_REF] Wargin | PHARMACOKINETICS OF METHYLPHENIDATE IN MAN, RAT AND MONKEY[END_REF][START_REF] Sauer | Clinical pharmacokinetics of atomoxetine[END_REF]. Between 90 and 135 minutes post-dose, six resting state arterial spin labelling scans were acquired, which will be reported separately. Approximately 135 minutes post-dose, gradient-echo (GE) echoplanar imaging was used to acquire 450 whole-brain images while participants performed a WM task (TR = 2s, TE = 30ms, FA = 75°, 38 3mm thick near-axial slices with 0.3mm gap, in-plane resolution = 3.75×3.75mm). A high-resolution GE structural scan was also acquired for each participant to assist accurate registration to a standard space (TR = 3s, TE = 30ms, FA = 90°, 43 3mm thick near axial slices with 0.3mm gap, in-plane resolution = 1.88×1.88mm).

Working memory task

During the WM task, 40 trials were presented with an inter-trial interval of 8 or 10 seconds and during each trial, participants were required to remember the spatial location of a target stimulus (a dot) relative to a fixation cross. The task allowed each WM component process (encoding, delay and retrieval) to be separately coded (Figure 1). Half the trials carried a monetary reward, indicated by the colour of the stimulus and the order of trials was randomised and counterbalanced across participants. During encoding (2 sec), the target stimulus was presented, followed immediately by a mask to disrupt visual iconic memory. After a variable length delay (7 or 9 sec), the target and an additional distractor stimulus were presented and participants indicated which of the stimuli matched the target location by left or right button press on a two-button response box (retrieval). At the conclusion of the trial, feedback was provided, indicating success or failure and responses and response time (RT) were recorded. Acquisition was optimised for volume-based PR, with stimuli presented in a TR-locked fashion, which ensures data vectors were sampled from approximately the same point on the haemodynamic response curve and helps to generate a consistent response pattern for each trial. The task was written in VB.net, presented via projector to a screen at the end of the scanner bed and viewed by participants through mirrors attached to the head coil.

Participants completed a visual analogue scale (VAS; [START_REF] Bond | The use of analogue scales in rating subjective feelings[END_REF] at four timepoints during each visit to record their subjective experience, which contained 16 items that were later collapsed to reflect two subjective factors: 'alertness' and 'tranquillity' [START_REF] Herbert | Factor-analysis of analog scales measuring subjective feelings before and after sleep[END_REF] supplementary material). Outside the scanner, VAS responses were measured with a ruler and inside the scanner a computerised VAS was administered where participants recorded their responses by moving a sliding cursor using the two-button response box.

[Figure 1 about here] FMRI data preprocessing FMRI data were realigned, spatially normalised and smoothed with an isotropic 8mm Gaussian kernel using SPM5 (www.fil.ion.ucl.ac.uk/spm). Additional pre-processing was performed in Matlab (www.mathworks.com), which consisted of linearly detrending the data and applying a whole-brain mask to select intracerebral voxels. Classifier samples were constructed by: (1) shifting the onset of each trial by one volume to accommodate the haemodynamic delay, (2) converting brain volumes acquired during each task component to vectors and (3) averaging two (encoding, retrieval and shorter delay) or three (longer delay) consecutive volumes from each WM component. We averaged at least two volumes for each WM component to accommodate the temporal blurring induced by the haemodynamic response and to ensure that we captured the peak of the haemodynamic response. Trials where each participant responded incorrectly were excluded and remaining trials were averaged to construct a single mean sample per participant (averaged over approximately 16 correct trials). We constructed classifier samples for the baseline condition by extracting and averaging two volumes during the fixation period between trials (6-8 seconds after the end of feedback).

Classifier implementation

We used binary Gaussian process classifiers (GPCs; [START_REF] Rasmussen | Gaussian Processes for Machine Learning[END_REF] to classify: (1) each WM component from baseline, (2) rewarded from non-rewarded trials and (3) each drug condition (ATX, MPH or PLC) from one another. GPCs are kernel classifiers similar to support vector machines (SVM) that have good performance for fMRI (Marquand et al., 2010b) and the main advantage of GPC over SVM is that GPCs provide probabilistic predictions and estimates of predictive uncertainty. Theoretical background and implementation details for GPC have been presented elsewhere [START_REF] Rasmussen | Gaussian Processes for Machine Learning[END_REF]Marquand et al., 2010b) but a brief description is provided in supplementary material. In this work we use linear kernel GPCs that help prevent overfitting and allow direct extraction of the weight vector as an image.

Recursive Feature Elimination

We embedded all classifiers contrasting reward or drug state in a recursive feature elimination framework (RFE; [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF], which is a backward elimination feature selection approach that aims to find a parsimonious set of features (voxels) by iteratively removing the least informative features. RFE was originally developed for SVM (SVM-RFE), and has been applied to multiple fMRI studies (e.g. De [START_REF] Martino | Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns[END_REF][START_REF] Formisano | Who" Is Saying "What"? Brain-Based Decoding of Human Voice and Speech[END_REF][START_REF] Hanson | Brain reading using full brain support vector machines for object recognition: There is no "Face" identification area[END_REF] but here we adapt it to GPC ('GPC-RFE'; Marquand et al., 2010a). RFE starts by creating an 'active feature set', initially containing all cerebral voxels. A classifier is trained repeatedly on the active set and at each iteration features are ranked and a subset of the lowest ranking features is removed (2% of voxels), which continues until no features remain. Predictive performance is measured at each stage of feature removal on an independent sample, allowing an optimal number of features maximising predictive performance to be selected (supplementary material). RFE is most commonly applied because it modestly increases accuracy, but here our main motivation was because it yields a spatially sparse multivariate map (akin to a thresholded statistical parametric map), which is essential to prevent falsely inferring a brain region is functionally important when in fact it is not.

RFE is a principled approach to achieve this goal and is more appropriate than an arbitrary voxelwise threshold because it: (1) validates the multivariate pattern against predictive accuracy, (2) accommodates the multivariate structure of the pattern and (3) does not require specification of an arbitrary threshold level. We did not apply GPC-RFE to the classifiers contrasting task and baseline, because this is a trivial classification problem and the objective was only to define the brain activity pattern evoked by the task for which an unthresholded map is preferable, but for reference purposes, we provide classification accuracy for whole-brain classifiers trained to discriminate between all experimental contexts (supplementary material).

Cross-validation

RFE can be viewed as a model selection problem, where model complexity is determined by a single parameter (the number of features to retain) which must be set without using the test dataset to avoid overfitting. To achieve this, we used nested leave-one-out cross-validation (LOO-CV), which uses a three-way split of the data to provide an unbiased estimate of generalisation ability while also allowing unbiased parameter estimation. For each LOO-CV fold, we excluded all data for a single participant for the test set, then repeatedly repartitioned the remaining 14 participants into a validation set (1 participant) and training set (13 participants). We selected the optimal number of features on the validation set before applying it to the test set.

Visualisation of the differential activity pattern

To visualise the differential activity patterns, we retrained each GPC-RFE classifier using all participants' data, where the optimal number of features was the mean across all training folds. For this application, we are interested in knowing how brain activity differs between experimental classes rather than providing a representation of the decision boundary, so we did not visualise classifier weights, which is common in PR [START_REF] Mourao-Miranda | Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data[END_REF]. Instead, we employed a mapping approach that enables direct visualisation of the relative class distribution, where the coefficient scores at each voxel represent the relative difference between experimental classes in the context of the entire pattern (Marquand et al., 2010b;supplementary material).

Results

Performance measures

Repeated-measures ANOVA revealed that reaction time (RT) did not differ between drugs (F 2,28 = 0.001, p = 0.99), or between rewarded and non-rewarded trials (F 1,14 = 0.003, p = 0.96), and there was no reward × drug interaction (F 2,28 = 0.47, p = 0.63). Participants made fewer errors on rewarded relative to non-rewarded trials (F 1,14 = 11.54, p < 0.01), but errors did not differ between drug conditions (F 2,28 = 0.12, p = 0.89) and there was no reward × drug interaction (F 2,28 = 1.80, p = 0.19). A summary of RT and accuracy is provided in supplementary table S1.

Subjective measures

Several participants reported side effects to the administration of the drugs (e.g. nausea, drowsiness) but these were mild in all cases and mostly resolved prior to discharge on the study day.

Subjective factor alertness and tranquillity were investigated as potential confounds to any drug effect using an independent repeated-measures ANOVA for each factor. For alertness, there was no main effect of drug (F 2,28 = 0.59, p = 0.56), but a main effect of timepoint was observed (F 1,14 = 8.83, p = 0.01), whereby post-dose VAS scores were slightly lower than pre-dose scores across all drug conditions. No drug × timepoint interaction was found (F 2,28 = 0.21, p = 0.81). For tranquillity, there was no main effect of drug (F 2,28 = 1.78, p = 0.19) or timepoint (F 1,14 = 0.02, p = 0.88) and no interaction effect (F 2,28 = 0.48, p = 0.62).

Task networks

Whole-brain classifiers accurately discriminated each WM component process from baseline for all drug conditions (mean accuracy (SEM) of 18 classifiers: 97.61% (0.01); p < 0.01, binomial test). As noted, the magnitude of GPC coefficients at each voxel provides a measure of the relative difference in BOLD activation between classes in the context of the entire discriminating pattern and the sign indicates ('favours') the class with greater mean activation (Marquand et al., 2010b). GPC distribution maps (supplementary figures S1-2) revealed a distributed network (pattern) favouring the task component processes including bilateral intraparietal sulci (IPS; Brodmann area (BA) 7), middle frontal gyri (BA 9/46) and bilateral medial and inferior frontal gyri (BA 6 and 47 respectively) in addition to visual and motor cortical regions. The pattern favouring baseline (task related deactivations -TRDs) included regions comprising the default mode network (DMN), i.e. posterior cingulate cortex (PCC; BA30), precuneus (BA 31), medial prefrontal cortex (PFC; BA 9/10 and 32), and lateral parietal cortex (BA 39).

Classification of reward

Classification accuracy for GPC-RFE classifiers discriminating between rewarded and non-rewarded trials exceeded chance (50%) for all WM component processes and across all three drug conditions with the exception of the encoding component on MPH (mean (SEM) of six classifiers: 70.72% (0.04); Figure 2A). The pattern favoured reward and encompassed both the WM networks and TRDs described above. Specifically, BOLD activity in lateral PFC, parietal regions, medial PFC and PCC/precuneus was relatively increased (Figure 3; Figure S2); in other words, the effect of reward was to attenuate TRDs and enhance activity in the WM network. TRDs were most prominently attenuated during encoding and delay components of the rewarded WM task whereas visual and WM regions were most prominently enhanced during delay and retrieval components of the task (See Figure 3). In summary, reward produces a generalised increase in BOLD activity, including both task-related activations (which increase with reward) and TRDs (which are suppressed with reward).

[Figure 2 about here]

[Figure 3 about here]

Classification accuracy for drug contrasts

For ATX vs. PLC, classification accuracy exceeded chance for encoding, delay and retrieval components of rewarded trials (p < 0.05), but not during any WM component for the non-rewarded trials (Figure 2B). For MPH vs. PLC, accuracy exceeded chance during encoding, delay and retrieval of rewarded trials and during encoding of non-rewarded trials (p < 0.05; Figure 2C). For MPH vs. ATX, classification accuracy exceeded chance for the delay component of rewarded trials (p < 0.05; Figure 2D). For all classifiers exceeding chance, RT data were used to explore putative relationships between classifier performance and behaviour. No significant correlations between RT and GPC-RFE predictive probabilities were found. Note that correlations with accuracy were not appropriate because all participants were well trained and made only a small number of errors, and only correct trials were included in the image analysis.

Discriminating pattern for ATX vs. PLC (rewarded trials)

Maps derived from classifiers trained to discriminate ATX from PLC on rewarded trials (Figure 4) contained a distributed pattern favouring PLC that included WM networks and DMN; in other words, in the reward context, ATX attenuated BOLD activity in WM networks and enhanced TRDs. During encoding, the pattern favouring PLC included the DMN (medial PFC and PCC/precuneus) and WM networks (IPS and bilateral PFC -BA 9, 46 and 47). In addition, small clusters weakly favouring ATX were observed in the cerebellum and lateral PFC during encoding. During delay and retrieval components, the pattern favouring PLC was most prominent in WM regions.

[Figure 4 about here]

Discriminating pattern for MPH vs. PLC (rewarded trials)

Maps derived from classifiers trained to discriminate MPH from PLC on rewarded trials (Figure 5) contained a distributed pattern favouring PLC similar to that observed for ATX, which also encompassed WM and DMN regions. During encoding the pattern favouring PLC was mostly localised to DMN regions, but during delay and retrieval, the PLC pattern additionally included clusters in WM, motor and visual regions and was most widespread during retrieval. The pattern favouring MPH was restricted to encoding and was localised mostly to the cerebellum and lateral PFC.

[Figure 5 about here]

Discriminating pattern for MPH vs. PLC (non-rewarded trials)

The map derived from the classifier trained to discriminate MPH from PLC during the encoding component of non-rewarded trials (Figure 6) contained a distributed pattern, this time favouring MPH, including DMN, WM (e.g. IPS) and visual regions. Thus, in the absence of reward, MPH enhanced activity in WM networks and attenuated TRDs. Note that the map contrasting MPH and PLC shows a strong qualitative similarity to the one contrasting rewarded and non-rewarded trials in the encoding component of the PLC condition (Figure 3A).

[Figure 6 about here]

Discriminating pattern for MPH vs. ATX (rewarded trials)

The map derived from the classifier trained to discriminate MPH from ATX on the delay component of rewarded trials (Figure 7) contained distributed patterns favouring MPH and ATX. The pattern favouring MPH was mainly localised to WM regions (IPS and lateral PFC -BA 9/46) and the pattern favouring ATX was mainly localised to the DMN. Thus, during the delay component of rewarded trials MPH relative to ATX resulted in greater BOLD activity in WM networks, and ATX relative to MPH resulted in greater TRDs.

[Figure 7 about here] For all contrasts, the differential patterns derived from GPC-RFE show a reasonably good correspondence to those derived from an equivalent univariate statistical parametric map (SPM), except the SPM retained substantially fewer voxels (at p < 0.001, uncorrected for multiple comparisons) than were retained by the classifier (data not shown).

A concise summary of this complex set of results is provided in table 1.

[Table 1 about here]

Discussion

We have demonstrated differential effects of MPH and ATX on brain activity patterns in healthy volunteers performing a rewarded WM task. An important conclusion from our results is that the The results in this study were determined by applying recently developed PR techniques to the neuroimaging data, which afford substantially greater sensitivity than conventional mass-univariate techniques [START_REF] Haynes | Decoding mental states from brain activity in humans[END_REF][START_REF] Norman | Beyond mind-reading: multi-voxel pattern analysis of fMRI data[END_REF] by making use of spatial correlation between voxels, lending themselves well to whole-brain inference. These properties make PR ideally suited to drug discrimination studies, where drugs administered systemically can theoretically influence distributed brain regions due to direct effects at target receptor sites and consequent effects on connected brain regions. It is important to emphasize that multivariate brain maps derived from PR analysis provide a different perspective to mass-univariate analysis and should be interpreted differently. In particular, multivariate brain maps describe a pattern of activity, and coefficients should not be interpreted as representing focal effects because many brain regions potentially contribute to the accuracy of the classifier.

The WM networks identified in this study agree well with previous studies [START_REF] Curtis | Maintenance of spatial and motor codes during oculomotor delayed response tasks[END_REF][START_REF] Gibbs | A functional MRI study of the effects of bromocriptine, a dopamine receptor agonist, on component processes of working memory[END_REF] and were sensitive to reward. During rewarded trials participants performed the task more accurately, which was reflected as a generalised pattern of increased brain activity throughout WM networks and in the DMN. Indeed, increased activity in WM brain regions is a known effect of reward on WM tasks [START_REF] Pochon | The neural system that bridges reward and cognition in humans: An fMRI study[END_REF][START_REF] Taylor | A functional neuroimaging study of motivation and executive function[END_REF][START_REF] Pessoa | Embedding reward signals into perception and cognition[END_REF] and may reflect an increase in neuronal effort.

Methylphenidate and atomoxetine did not alter performance accuracy or response latency during the WM task. However, previous studies using MPH and amphetamine have suggested that reductions in BOLD activation accompanied by equivalent behavioural performance reflect an increased efficiency of WM networks [START_REF] Mattay | Effects of dextroamphetamine on cognitive performance and cortical activation[END_REF][START_REF] Mehta | Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human brain[END_REF]. Thus, for our data in a rewarded context, this would seem to be the most parsimonious explanation for the effects of MPH and ATX on task activation and deactivation networks. This effect is probably mediated by increased catecholamine concentrations in WM regions [START_REF] Bymaster | Atomoxetine increases extracellular levels of norepinephrine and dopamine in prefrontal cortex of rat: A potential mechanism for efficacy in Attention Deficit/Hyperactivity Disorder[END_REF], which is known to focus neuronal activity by enhancing responses to task-relevant stimuli while suppressing background noise [START_REF] Foote | Effects of Putative neurotransmitters on neuronal activity in monkey auditory cortex[END_REF][START_REF] Seamans | Bidirectional dopamine modulation of GABAergic inhibition in prefrontal cortical pyramidal neurons[END_REF]. Historically, DA has been linked with WM performance through increasing the efficiency of PFC neurons by decreasing delay related response to "noise" [START_REF] Arnsten | Catecholamine and second messenger lnfluences on prefrontal cortical networks of "representational knowledge": A rational bridge between genetics and the symptoms of mental illness[END_REF][START_REF] Vijayraghavan | Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory[END_REF] and the stabilisation of their sustained activity [START_REF] Durstewitz | Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex[END_REF]. However, NA is probably also important as therapeutic doses of MPH increase PFC extracellular concentrations of NA substantially more than DA [START_REF] Berridge | Methylphenidate preferentially increases catecholamine neurotransmission within the prefrontal cortex at low doses that enhance cognitive function[END_REF], and the beneficial effects of MPH and ATX on WM can be blocked by either DA D1 or NA α2 receptor antagonists [START_REF] Arnsten | Methylphenidate improves prefrontal cortical cognitive function through α2 adrenoreceptors and dopamine D1 receptor actions: Relevance to therapeutic effects in Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Gamo | methylphenidate and atomoxetine enhance prefrontal function through alpha-2 adrenergic and dopamine D1 receptors[END_REF]. NA is also known to increase delayrelated activity of PFC neurons in response to "signals" [START_REF] Arnsten | Catecholamine and second messenger lnfluences on prefrontal cortical networks of "representational knowledge": A rational bridge between genetics and the symptoms of mental illness[END_REF] and increase the salience of novel stimuli leading to the suggestion that it serves as an alarm system for contextual changes (Yu and Dayan, 2005).

The PCC, precuneus and ventromedial PFC are known to show decreased activity during a wide range of goal-directed tasks [START_REF] Shulman | Common blood flow changes across visual tasks .2. Decreases in cerebral cortex[END_REF]. These regions have been proposed to underlie a 'default mode' of brain function [START_REF] Raichle | A default mode of brain function[END_REF] and it is thought that to facilitate goal-directed action task-irrelevant mental activity in these regions must be suppressed. Indeed, failure to suppress default mode activity reflects momentary lapses in attention [START_REF] Weissman | The neural bases of momentary lapses in attention[END_REF] resulting in increased probability of error [START_REF] Eichele | Prediction of human errors by maladaptive changes in event-related brain networks[END_REF]. There is also preliminary evidence that ADHD may be characterised by deficiencies in attentional focus and insufficient suppression of brain activity in focal regions of the DMN [START_REF] Fassbender | A lack of default network suppression is linked to increased distractibility in ADHD[END_REF] and that MPH may normalise the amplitude of TRDs in treatment-responsive ADHD participants [START_REF] Peterson | An fMRI Study of the Effects of Psychostimulants on Default-Mode Processing During Stroop Task Performance in Youths With ADHD[END_REF]. Our results are consistent with this interpretation and further show that suppression of task-irrelevant mental activity may be a mechanism common to both MPH and ATX. Importantly, this effect was contextdependent, as it was only observed during rewarded trials.

In a rewarded context, classification accuracy was equivalent for classifiers discriminating MPH or ATX from PLC for each WM component, although accuracy was slightly higher for both drugs during retrieval relative to encoding and delay. Qualitatively, the effects of MPH and ATX were comparable, with both drugs producing a generalised decrease in brain activity in WM networks and DMN (i.e. attenuation of activity in WM networks and enhancement of TRDs). Nonetheless, the extent of these effects separated the drugs during the delay component of rewarded trials: ATX attenuated BOLD activity in WM networks more than MPH and MPH enhanced TRDs more than ATX.

Microdialysis studies in rodents have shown that MPH and ATX increase DA concentration in the PFC, but only MPH increases DA in the striatum [START_REF] Bymaster | Atomoxetine increases extracellular levels of norepinephrine and dopamine in prefrontal cortex of rat: A potential mechanism for efficacy in Attention Deficit/Hyperactivity Disorder[END_REF] and that therapeutic doses of MPH increase catecholamine concentration in the PFC substantially more than in the striatum [START_REF] Berridge | Methylphenidate preferentially increases catecholamine neurotransmission within the prefrontal cortex at low doses that enhance cognitive function[END_REF]. However, in our study we did not observe increased striatal activity following MPH, similar to other neuroimaging studies in healthy volunteers [START_REF] Mehta | Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human brain[END_REF][START_REF] De Haes | Methylphenidate-induced activation of the anterior cingulate but not the striatum: a 15O H2O PET study in healthy volunteers[END_REF]. This may be because the WM task we employed does not substantially engage the striatum, even for rewarded trials (Supplementary Figure S2), which is consistent with a recent review of the effects of reward on WM [START_REF] Pessoa | Embedding reward signals into perception and cognition[END_REF] or simply because the consequential effects of MPH on striatal DA levels are expressed in connected brain regions. Thus, subcortical effects of MPH on DA remain a candidate mechanism for the differential effects of MPH and ATX, as the PFC and striatum are strongly connected by parallel corticostriatal circuits [START_REF] Alexander | Parallel Organization of functionally segregated circuits linking basal ganglia and cortex[END_REF] and there is emerging evidence suggesting that the striatal DA system plays a role in the modulation of the DMN [START_REF] Kelly | L-Dopa Modulates Functional Connectivity in Striatal Cognitive and Motor Networks: A Double-Blind Placebo-Controlled Study[END_REF][START_REF] Tomasi | Dopamine Transporters in Striatum Correlate with Deactivation in the Default Mode Network during Visuospatial Attention[END_REF]. However, studies concurrently measuring striatal DA release and its functional consequences on brain activity are required to test this hypothesis explicitly.

In a non-rewarded context, it was only possible to discriminate MPH from PLC during encoding.

In this case, the differential pattern (Figure 6) bears a strong qualitative resemblance to that differentiating rewarded from non-rewarded trials (Figure 3) suggesting that while MPH didn't improve performance at the dose administered MPH nevertheless mimics the reward effect.

Discrimination accuracies for classifiers contrasting rewarded and non-rewarded trials were also consistently lower on MPH than on ATX or PLC and did not exceed chance for encoding, indicating that activity patterns discriminating reward and non-rewarded trials were less distinguishable on MPH (Figure 2A), which is consistent with the suggestion that MPH increases task salience [START_REF] Volkow | Evidence that methylphenidate enhances the saliency of a mathematical task by increasing dopamine in the human brain[END_REF]. This effect is probably mediated by DA, because a learned association between a cue and a reward results in increased phasic dopaminergic firing during cue presentation not reward delivery [START_REF] Schultz | Responses of monkey dopamine neurons to reward and conditioned-stimuli during successive steps of learning a delayed-response task[END_REF]and increased dopaminergic firing, often followed by immediate depression, is also associated with stimuli that resemble the rewarded stimulus [START_REF] Schultz | Dopamine neurons of the monkey midbrain -contingencies of responses to stimuli eliciting immediate behavioural reactions[END_REF]. Catecholaminergic signalling has also been associated with an 'inverted-U' dose-response relationship in the PFC [START_REF] Arnsten | Stimulants: Therapeutic actions in ADHD[END_REF][START_REF] Levy | Dopamine vs noradrenaline: inverted-U effects and ADHD theories[END_REF] with optimal PFC function at intermediate concentrations and too much or too little DA or NA resulting in impaired PFC function. Although speculative at this stage, such a relationship may underlie different contextual effects of MPH, where rewarded and non-rewarded contexts may engage curves with different optimal dosing. Also, we only administered one dose of each drug here, so it is possible that ATX shares the reward emulating effect at a different dose, which could additionally account for the classifier's inability to discriminate MPH and ATX during encoding of non-rewarded trials (Supplementary Figure S4).

Individual differences in response to drug administration may be an interesting line of future research. In particular, genetic factors influence responses to stimulants [START_REF] Mattay | Catechol O-methyltransferase val(158)-met genotype and individual variation in the brain response to amphetamine[END_REF] and although we did not collect genetic information here, inclusion of genetic factors can only be expected to improve predictive performance. As noted, only one dose of each drug was administered so dose effects cannot be excluded as confounds, but three lines of evidence speak against this possibility: first, administered doses were matched according to doses used in clinical practice. Second, motor evoked potentials were altered to a similar extent for both drugs using identical doses to those administered here [START_REF] Gilbert | Comparison of the inhibitory and excitatory effects of ADHD medications methylphenidate and atomoxetine on motor cortex[END_REF]. Third, opposing effects of MPH and ATX on activated and deactivated task networks during the delay component of rewarded trials are difficult to explain by a simple dose effect.

In summary, we accurately discriminated the effects of MPH and ATX on rewarded and nonrewarded WM networks using multivariate PR. We suggest that this method is ideal for drug discrimination studies because for most psychotropic medications subtle distributed effects Tables Table 1: Summary of classification results Tables Table 1: Summary of classification results

Contrast

Contrast

  effects of MPH and ATX on WM are context-dependent. In the rewarded context, both MPH and ATX could be accurately discriminated from PLC across all task components, showing similar patterns of attenuation across the WM networks and enhanced TRDs. During the encoding component of nonrewarded trials, MPH, but not ATX, could be discriminated from PLC; MPH increased activity in WM regions and attenuated TRDs compared to PLC. The pattern of BOLD signal changes observed during the delay component of rewarded trials also discriminated MPH from ATX. In this context, and relative to ATX, MPH produced a pattern of increased activity in WM networks, while ATX produced greater activity in the DMN. Overall this complex set of findings suggests that: (1) both MPH and ATX have salient effects during rewarded WM in both task activated and deactivated networks, (2) During the delay component of rewarded trials, MPH and ATX had opposing effects on activated and deactivated networks and (3) MPH may mimic reward during encoding.

FiguresFigure 1 :Figure 2 :

 12 Figure 3, Figure S2

Figure 3 :

 3 Figure 3: GPC-RFE distribution maps for classifiers discriminating between rewarded and nonrewarded trials for each WM component (PLC arm). A: encoding B: delay C: retrieval. Maps were rescaled such that the absolute maximum coefficient score was +/-1. The magnitude of GPC coefficients provides a measure of the relative difference in BOLD activity between experimental classes (in the context of the entire pattern) and the sign favours the class with greater mean activity. A distributed pattern favouring reward can be observed that indicates: (1) reward increased activity throughout WM networks and across all WM component processes (2) reward attenuated TRDs in DMN regions, which was especially prominent during encoding and delay.

Figure 4 :

 4 Figure 4: GPC-RFE distribution maps for classifiers discriminating between ATX and PLC conditions for each WM component (rewarded trials). A: encoding B: delay C: retrieval. A distributed pattern favouring PLC can be observed that indicates that in a rewarded context: (1) ATX attenuated activity throughout WM networks, which was most prominent during delay and retrieval (2) ATX enhanced TRDs in DMN regions across all WM component processes. The cerebellum was the only region favouring ATX and was only observed during encoding.

Figure 5 :

 5 Figure 5: GPC-RFE distribution maps for classifiers discriminating between MPH and PLC for each WM component (rewarded trials). A: encoding B: delay C: retrieval. A distributed pattern favouring PLC can be observed that indicates that in a rewarded context: (1) MPH attenuated activity throughout WM networks, which was most prominent during delay and retrieval (2) MPH enhanced TRDs in DMN regions across all WM component processes. The only regions favouring MPH were found during encoding and included cerebellum and small regions of lateral PFC.

Figure 6 :

 6 Figure 6: GPC-RFE distribution maps for classifiers discriminating between MPH and PLC for the encoding WM component (non-rewarded trials). A distributed pattern favouring MPH can be observed that indicates that during encoding and in a non-rewarded context MPH enhanced activity in some WM regions and enhanced TRDs in DMN regions.

Figure 7 :

 7 Figure 7: GPC-RFE distribution maps for classifiers discriminating between MPH and ATX for the delay component of rewarded trials. Distributed patterns of activity favouring both MPH and ATX can be observed that indicate that in a rewarded context: (1) MPH enhanced activity throughout WM networks relative to ATX and (2) ATX enhanced TRDs in DMN regions.

  Figure 1

  Figure 3, Figure S2

Yu AJ, Dayan P (2005) Uncertainty, neuromodulation, and attention. Neuron 46:681-692.

Acknowledgements

The authors thank Bill Vennart, Paul Maguire and Caroline Woodlridge from Pfizer Global Research and Development and the Wellcome Trust and EPSRC for continued funding of the Centre for Neuroimaging Sciences. JMM was funded by the Wellcome Trust and AM gratefully acknowledges support from King's College Annual Fund.

probably predominate over strong focal effects. More importantly, our results demonstrate that MPH and ATX have effects on WM function that are context-dependent and suggest that the interaction between drug effects and motivational state will be crucial in defining the beneficial effects of MPH and ATX in ADHD.

Disclosure/Conflict of Interest

The authors declare no conflict of interest.


MD, PhD Tina Parkner
email: tparkner@dadlnet.dk

MD, PhD Jannik Kruse Nielsen

MD Thomas Damgaard Sandahl

PhD Bo Martin Bibby

RD Birgit Schelde Jensen

DMSc Jens Sandahl Christiansen

Do all patients with type 2 diabetes need breakfast?

Keywords: Type 2 Diabetes Mellitus, Diabetic Diet, Diet

Objectives: To evaluate if an improved daily glycaemic profile could be achieved in patients with type 2 diabetes by withholding breakfast, but maintaining the same total daily intake of calorie and the same composition of carbohydrates, fat, and protein.

Methods: Thirteen type 2 diabetic patients participated in this randomised crossover study.

Following an initial fasting night the study consisted of 4 consecutive days. Patients were randomised to diets including breakfast day 1 and 3, and excluding breakfast day 2 and 4, or vice versa.

Results:

The mean plasma glucose level was 0.24 mmol/l higher after the breakfast diet compared to the non-breakfast diet, but reflected only a tendency (P=0.066). The standard deviation based on plasma glucose was 32% higher after the breakfast diet compared to the non-breakfast diet (P<0.0001).

Conclusions: Not all patients with type 2 diabetes may need breakfast. Moreover, a non-breakfast diet reduces glycemic variability.

Main text:

Hyperglycemia after breakfast occurs in many patients with type 2 diabetes even when treated with oral antidiabetics or/and insulin (American Diabetes Association 2001). Considerable attention has been focused on the importance of postprandial hyperglycemia as a risk factor for cardiovascular disease [START_REF] Gerich | Clinical significance, pathogenesis, and management of postprandial hyperglycemia[END_REF]) and as a rate-limiting factor for achieving optimal glycemic control in these patients [START_REF] Woerle | Impact of fasting and postprandial glycemia on overall glycemic control in type 2 diabetes Importance of postprandial glycemia to achieve target HbA1c levels[END_REF]). Moreover, achieving minimal intra-individual within-day and day-today glucose variability is also important for tailored sufficient treatment and seems to reduce diabetic complications (Monnier et al. 2008). However, consensus data on favourable frequency of meals including need of breakfast or not for patients with type 2 diabetes are unavailable.

Thirteen patients with type 2 diabetes were recruited (Table 1). Inclusion criteria were: Type 2 diabetic patients treated with diet and/or one or two antidiabetic drugs. Exclusion criteria were: Not insulin naive patients and concomitant disorders or medications judged to influence the study.

The trial was a randomised crossover study. The patients were hospitalised the evening before the first of 4 consecutive study days in the hospital setting. They received a standardized evening meal and they fasted from 10 pm. The next morning the study began at 8 am. Each patient received a randomised diet schedule that is begun on a day with (7 patients) or without breakfast (6 patients).

Every other day the breakfast regime shifted. At the 2 separated breakfast days the daily energy distribution were as following: At breakfast (8 am) 25%, at lunch (12 noon) 25%, at first snack (3 pm) 10%, at dinner (6 pm) 30% and at a late snack (8 pm) 10% of the daily total energy intake was consumed, whereas on the 2 days without breakfast the distribution were 0%, 35%, 15%, 35% and 15%, respectively. The standardised meals were adjusted to ensure the same total daily energy intake on all study days.

Prior to the study three different energy levels for daily diet regimes were defined: 8372 kJ/day, 10465 kJ/day or 12558 kJ/day. Each patient was assigned to one of these energy levels for the 4 study days based on each patient's basic metabolic rate multiplied by an activity factor of 1.3. Each meal contained 55% of the energy as carbohydrate, 30% as fat and 15% as protein. Moreover, each participant chose between walks for half an hour 2 or 3 times per day. For each patient exercise was to be exactly the same and to be carried out on the same time on all days.

Plasma glucose profiles were measured by bedside standard plasma calibrated GlucoMeter (Accu-Chek ® ) each hour from 8:00 am-8:00 pm and one time during the night at 3 am.

The mean plasma glucose level based on measurements over the whole day (8 am-3 am) was 0.24 mmol/l higher after the breakfast diet compared to the fasting diet, though only reflecting a tendency (95%-CI:-0.02-0.50 mmol/l; P=0.066) (Figure 1). When based only on the measurements obtained before lunch (8 am-12 noon), the mean plasma glucose level was clearly significantly higher after the breakfast diet compared to the fasting diet (1.44 mmol/l, 95%-CI:1.10-1.79 mmol/l; P<0.0001). After lunch (1 pm-3 am) the mean plasma glucose level was significantly higher after the fasting diet compared to after the breakfast diet (0.43 mmol/l, 95%-CI:0.09-0.77 mmol/l; P=0.0153).

The standard deviation based on plasma glucose measurements for the whole day (8 am-3 am) was 32 % higher on the diet including breakfast compared to the fasting diet (95%-CI:18-49%; P<0.0001). Before lunch (8 am-12 noon) the difference between the breakfast diet and the fasting diet regarding the standard deviation was even more pronounced (260% higher after breakfast, 95%-CI:172-378%; P<0.0001). When based solely on plasma glucose measurements after lunch (1 pm-3 am) there was no significant difference between the two diets (2% smaller after the breakfast diet, 95%-CI:-17-16%; P=0.84).

The American Diabetes Association recommends peak post-prandial plasma glucose (PPPG) to be <10 mmol/l. The International Diabetes Federation (IDF) recommends near normal 2-hour PPPG values of ≤7.8 mmol/l (International Diabetes Federation 2007). Our data show mean post-prandial breakfast glucose levels exceeding the IDF recommendation on breakfast days but not on the fasting diet. An interesting observation was that none of the patients felt hungry during the mornings with fasting and no hypoglycaemic events were recorded during these mornings.

Post-prandial hyperglycemia, especially in the lower categories of HbA 1c, has been suggested as an independent risk factor for macrovascular disease, the major cause of death in type 2 diabetes [START_REF] Cavalot | Postprandial blood glucose is a stronger predictor of cardiovascular events than fasting blood glucose in type 2 diabetes mellitus, particularly in women: lessons from the San Luigi Gonzaga Diabetes Study[END_REF]. Furthermore, in most of these patients, the post-meal excursions are longer than normal and follow an ordinal decreasing scale that can be described as post-breakfast > postdinner or post-lunch excursions [START_REF] Monnier | The loss of postprandial glycemic control precedes stepwise deterioration of fasting with worsening diabetes[END_REF]. This is in accordance with our study at breakfast diet days. However, the mean plasma glucose level based on measurements over the whole day was higher after the breakfast diet compared to the fasting diet, but reflected only a tendency. Moreover, weaknesses of our small pilot study include a lack of data from 3 am-8 am and not using a continuous glucose monitoring system for measuring the complete plasma glucose profiles [START_REF] Monnier | Glycemic variability: the third component of the dysglycemia in diabetes. Is it important? How to measure it?[END_REF]. It should also be acknowledged that some data have suggested that less frequent meals, and especially lack of breakfast, are associated with weight gain [START_REF] Berg | Eating patterns and portion size associated with obesity in a Swedish population[END_REF]).

The glycemic variability, based on plasma glucose measurements for the whole day, was significantly higher after the breakfast diet compared to the non-breakfast diet. Before lunch the difference was even more pronounced, which was expected, as patients with type 2 diabetes in particular have various degrees of insulin resistant in the morning hours. Thus, the glycemic disorders in type 2 diabetes are not limited to hyperglycemia but can be extended to the glycemic variability that includes both upward and downward glucose changes (Monnier and Colette 2008).

In type 2 diabetes blood glucose fluctuations have been found to be relevant to the progression of arterosclerosis and to correlation strongly with macrovascular complications [START_REF] Decode | Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria[END_REF]. Moreover, it has been demonstrated that intra-day blood glucose excursions may have a pronounced effect on carotid intima-media thickness progression [START_REF] Chen | Correlation between glucose fluctuations and carotid intima-media thickness in type 2 diabetes[END_REF]. This could suggest that glucose variations may be a major contributor to the development of vascular injury.

In conclusion, not all patients with type 2 diabetes may need breakfast. Moreover, a non-breakfast diet seems to reduce glycemic variability.

Figure 1 :

 1 Figure 1: Mean plasma glucose (mmol/l) with 95%-confidens intervals for the thirteen patients with type 2 diabetes during the 4 consecutive study days with or without breakfast, respectively.

Conflict of interest:

None of the authors have any competing financial interests in relation to the study described.

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