Psychological Medicine, Page 1 of 11. doi:10.1017/S0033291714002232

OR I G I N A L A R T I C L E

© Cambridge University Press 2014

Disorder-dissociated effects of fluoxetine on brain function of working memory in attention deficit hyperactivity disorder and autism spectrum disorder K. Chantiluke1, N. Barrett2, V. Giampietro3, M. Brammer3, A. Simmons3,4 and K. Rubia1* 1

Department of Child and Adolescent Psychiatry, Institute of Psychiatry, King’s College London, UK South London and Maudsley NHS Trust, London, UK 3 Department of Neuroimaging, Institute of Psychiatry, King’s College London, UK 4 NIHR Biomedical Research Centre for Mental Health at South London and Maudsley NHS Trust, London, UK 2

Background. Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are often co-morbid and share performance and brain dysfunctions during working memory (WM). Serotonin agonists modulate WM and there is evidence of positive behavioural effects in both disorders. We therefore used functional magnetic resonance imaging (fMRI) to investigate shared and disorder-specific brain dysfunctions of WM in these disorders, and the effects of a single dose of the selective serotonin reuptake inhibitor (SSRI) fluoxetine. Method. Age-matched boys with ADHD (n = 17), ASD (n = 17) and controls (n = 22) were compared using fMRI during an N-back WM task. Patients were scanned twice, under either an acute dose of fluoxetine or placebo in a double-blind, placebo-controlled randomized design. Repeated-measures analyses within patients assessed drug effects on performance and brain function. To test for normalization effects of brain dysfunctions, patients under each drug condition were compared to controls. Results. Under placebo, relative to controls, both ADHD and ASD boys shared underactivation in the right dorsolateral prefrontal cortex (DLPFC). Fluoxetine significantly normalized the DLPFC underactivation in ASD relative to controls whereas it increased posterior cingulate cortex (PCC) deactivation in ADHD relative to control boys. Within-patient analyses showed inverse effects of fluoxetine on PCC deactivation, which it enhanced in ADHD and decreased in ASD. Conclusions. The findings show that fluoxetine modulates brain activation during WM in a disorder-specific manner by normalizing task-positive DLPFC dysfunction in ASD boys and enhancing task-negative default mode network (DMN) deactivation in ADHD. Received 8 July 2014; Revised 13 August 2014; Accepted 13 August 2014 Key words: ADHD, ASD, fMRI, serotonin, working memory.

Introduction Attention deficit hyperactivity disorder (ADHD) is defined by age-inappropriate levels of inattention, impulsivity and hyperactivity whereas autism spectrum disorder (ASD) is defined by impairments in communication and social interaction and by restricted, repetitive behaviour (APA, 1994). There is increasing evidence for co-morbidity between the disorders (Rommelse et al. 2011), which share deficits in executive functions (Willcutt et al. 2005; Corbett et al. 2009), including working memory (WM), particularly at higher WM loads (Cui et al. 2010; Kasper et al. 2012).

* Address for correspondence: Professor K. Rubia, Department of Child Psychiatry, MRC Centre for Social, Genetic and Developmental Psychiatry (SGDP), PO46, Institute of Psychiatry, King’s College London, 16 De Crespigny Park, London SE5 8AF, UK. (Email: [email protected])

The importance of this clinical and behavioural overlap is highlighted by recent changes to DSM-5, allowing co-diagnosis of ADHD and ASD (www.dsm5.org). WM refers to the ability to temporarily store and manipulate information to guide and direct behaviour (Baddeley, 1996). WM can be measured in the N-back task, where subjects identify targets that were shown a few trials back (Baddeley, 2003). ADHD adolescent studies have shown underactivation during verbal N-back tasks compared to controls in the bilateral dorsolateral prefrontal cortex (DLPFC), parietal lobe and cerebellum, with more pronounced behavioural and functional deficits during higher WM loads (Kobel et al. 2009; Cubillo et al. 2014). In ASD adults, a functional magnetic resonance imaging (fMRI) study found decreased activation in the left hemispheric DLPFC, inferior frontal cortex (IFC) and inferior parietal lobe during the highest WM load of a verbal N-back task, but this has not been found in children

2 K. Chantiluke et al. (Koshino et al. 2005). An important question, however, is whether children with ASD have the same brain dysfunctions as adults with ASD and whether these deficits are shared or are disorder specific relative to ADHD. Serotonin (5-hydroxytryptamine or 5-HT) mediates verbal WM (Allen et al. 2006; Rose et al. 2006). Pharmacological fMRI studies using the N-back task in healthy adults have shown that selective serotonin reuptake inhibitors (SSRIs), which enhance 5-HT levels, increase left IFC activation (Rose et al. 2006) whereas acute tryptophan depletion (ATD), which reduces 5-HT synthesis (Nishizawa et al. 1997), decreases right middle/IFC activation and attenuates default mode network (DMN) deactivation during high WM loads (Allen et al. 2006). Few studies have investigated the clinical efficacy of SSRIs in ADHD and ASD. Open-label studies of fluoxetine have shown improvement in ADHD symptoms in co-morbid ADHD children (Barrickman et al. 1991; Gammon & Brown, 1993; Quintana et al. 2007). Fluoxetine improved ASD symptoms in children (West et al. 2009) and adults with ASD (Williams et al. 2010; Hollander et al. 2012), with a meta-analysis showing improvements in repetitive behaviours (Carrasco et al. 2012). However, negative findings with other SSRIs have also been reported (King et al. 2009). Abnormalities in 5-HT have been reported in both disorders. Approximately 30% of ASD patients have enhanced platelet 5-HT levels (Hranilovic et al. 2007) and reduced binding to the 5-HT reuptake transporter and 5-HT2A receptors in the frontal lobe and PCC, which have been linked to poor social communication (Murphy et al. 2006; Nakamura et al. 2010). ADHD patients have lower platelet 5-HT levels relative to controls (Spivak et al. 1999), along with genetic (Gizer et al. 2009) and biochemical 5-HT abnormalities (Oades, 2007). Furthermore, both animal and human studies have shown an association between 5-HT and impulsiveness (Dalley & Roiser, 2012) and between 5-HT and stimulant medication response (Gainetdinov et al. 1999; McGough et al. 2009). Neurotransmitter abnormalities underlie abnormal neurocognitive functioning. An important question that may help to elucidate potential disorder-specific neurotransmitter underpinnings of neurocognitive abnormalities is whether 5-HT agonists modulate verbal WM networks in both disorders in a shared or disorder-specific manner. An investigation of acute effects can reveal true drug effects, not confounded by indirect effects on symptom improvement after chronic administration. A better understanding of the shared and disorder-specific neurofunctional and neurotransmitter abnormalities in ADHD and ASD is of clinical importance as it may ultimately aid future

differential diagnosis and improve disorder-specific treatment. The aim of this fMRI study was therefore to investigate (1) shared and disorder-specific brain dysfunctions in children with ADHD and children with ASD during a verbal N-back task relative to controls and each other, and (2) shared and disorder-specific neurofunctional effects of an acute dose of fluoxetine on these relative to placebo. Based on previous findings, we hypothesized that, under placebo, relative to controls, both disorders would show reduced left DLPFC and parietal activation (Koshino et al. 2005; Kobel et al. 2009; Cubillo et al. 2014), with ADHD patients exhibiting additional right DLPFC and cerebellar abnormalities (Koshino et al. 2005; Kobel et al. 2009; Cubillo et al. 2014). Based on evidence for decreased serotonin levels in ADHD (Barrickman et al. 1991; Gammon & Brown, 1993; Quintana et al. 2007), we hypothesized that increased serotonin levels under fluoxetine would increase frontal brain activation in ADHD, as observed previously in pharmaco-fMRI studies of verbal WM and serotonin agonists in healthy adults (Allen et al. 2006; Rose et al. 2006). Although there is some evidence for higher platelet levels of serotonin in ASD (Hranilovic et al. 2007), the poor binding of serotonin to 5-HT receptors in the frontal lobe suggests that this may be ineffective at eliciting frontal activation (Murphy et al. 2006; Nakamura et al. 2010). Therefore, we hypothesized that a fluoxetinemediated increase in serotonin would also enhance frontal activation in ASD.

Method Participants Fifty-six right-handed boys (Edinburgh Handedness Inventory; Oldfield, 1971) aged 10–17 years (22 controls, 17 with ADHD and 17 with ASD), with IQ > 70 [Wechsler Abbreviated Scale of Intelligence – Revised (WASI-R; Wechsler, 1999], participated in this study. ADHD boys had a clinical DSM-IV diagnosis of nonco-morbid ADHD, inattentive/hyperactive-impulsive combined subtype, assessed using the standardized Maudsley diagnostic interview (Goldberg & Murray, 2002). They scored above the clinical threshold for ADHD symptoms on the Strengths and Difficulties Questionnaire (SDQ; Goodman & Scott, 1999) and the revised Conners’ Parent Rating Scale (CPRS-R; Conners et al. 1998); one boy was below the cut-off on the SDQ but had diagnostic confirmation from a child psychiatrist. Three ADHD boys were medication naïve, one had stopped methylphenidate for 3 months and 13 had received chronic stimulants but had a 48-h medication wash-out prior to scanning. To ensure

Fluoxetine and working memory in ADHD and ASD 3 Table 1. Sample characteristics for healthy control boys and boys with ADHD and ASD Variables

Controls (n = 22)

ADHD (n = 17)

ASD (n = 17)

Age (years) IQ SDQ Hyperactive-impulsive/inattentive subscale SDQ Emotional distress subscale SDQ Conduct subscale SDQ Peer relationships subscale SDQ Prosocial behaviour subscale SDQ Total scores SCQ Total CPRS-R Total T score ADOS Communication score ADOS Social interaction score ADOS Communication and social score ADOS Stereotyped behaviour score ADI Communication score ADI Social interaction score ADI Stereotyped score

14.0 (2.6) 111 (13) 2.0 (1.8) 0.9 (1.6) 0.5 (0.9) 0.9 (1.2) 8.7 (2.3) 4.3 (4) 1.8 (2.8) 44 (4) – – – – – – –

14.7 (1.9) 93 (9) 8.9 (1.4)a 3.2 (3.0) 4.6 (2.1)a 3.6 (2.3) 6.3 (2.3) 20.4 (4.9) 8.1 (4.1) 83 (8)a – – – – – – –

15.2 (1.8) 114 (14) 4.7 (1.9) 4.3 (3.0) 2.2 (2.1) 6.1 (2.4)b 4.8 (2.2) 17.4 (5.9) 23.4 (5.6)b 59 (9) 2 (1) 7 (4) 9 (5) 1 (1) 15 (4) 17 (4) 5 (3)

ADHD, Attention deficit hyperactivity disorder; ASD, autism spectrum disorder; SDQ, Strength and Difficulties Questionnaire; SCQ, Social Communication Questionnaire; CPRS-R, revised Conners’ Parent Rating Scale; ADOS, Autism Diagnostic Observation Schedule; ADI, Autism Diagnostic Interview. a and b indicate a significant difference between groups. aRefers to a significantly enhanced value in ADHD relative to controls and ASD patients (p < 0.05). bRefers to a significantly higher level in ASD relative to controls and ADHD patients (p < 0.05). Values are given as mean (standard deviation).

there was no co-morbidity with ASD, ADHD boys had to score below 15 on the Social Communication Questionnaire (SCQ; Rutter et al. 2003). ASD diagnosis was made using ICD-10 (WHO, 1994) diagnostic criteria and confirmed by the Autism Diagnostic Interview – Revised (ADI-R; Lord et al. 1994) and the Autism Diagnostic Observation Schedule (ADOS; Lord et al. 2000). All ASD subjects were medication naïve except one, who took melatonin, but underwent a 2-week medication wash-out. To ensure no co-morbidity with ADHD, the ASD boys had to score below 7 on the Hyperactivity/ Inattention subscale of the SDQ. Exclusion criteria were co-morbidity with other psychiatric or neurological disorders, and drug/alcohol dependency, as assessed by the standardized Maudsley diagnostic interview. Patients were recruited from local clinics and support groups. Patients were scanned twice in a double-blind, counterbalanced randomized, placebo-controlled design. Because of the half-life of fluoxetine (1–3 days) and its metabolite (5–16 days) (Wong et al. 1995), scans were 3–4 weeks apart. To ensure that fluoxetine had reached its peak plasma level, after 5–8 h (Wong et al. 1995), patients were scanned 5 h after administration. During this 5-h time period, patients practised the N-back task once each time, and underwent IQ and

clinical assessments (the first time), had lunch and rested. Liquid fluoxetine was titrated to age and weight: boys aged between 10 and 13 years and weighing < 30 kg received 8 mg, those weighing > 30 kg received 10 mg. Boys aged between 14 and 17 years and weighing < 30 kg received 10 mg, those > 30 kg received 15 mg. Placebo was equivalent amounts of peppermint water with a similar taste to fluoxetine. Twenty-two healthy, right-handed and age-matched boys were recruited by advertisement and scored below clinical thresholds on the SDQ, SCQ and CPRS-R. Written informed consent/assent was obtained from all participants. The study was approved by the local ethics committee. Participants were paid £50 for each scan. For recruitment, demographic and clinical details of participants, see Table 1 and the online Supplementary material. fMRI paradigm Subjects practised the 6-min block design WM task once before scanning (Ginestet & Simmons, 2011; Cubillo et al. 2014). During the 1-back, 2-back and 3-back conditions, subjects are presented with series of letters (A–Z) (1 s duration; intertrial interval 2 s)

4 K. Chantiluke et al. and must respond with a button press (using their right thumb) whenever the letter presented is the same as one, two or three before it respectively (e.g. 2-back: B/J/A/J). This requires both storage and continuous updating of stimuli being held in WM. In the baseline vigilance 0-back condition, subjects must respond to each X that appears on the screen. The task consists of 12 randomized blocks. Before each block, written instructions (3 s) indicate which condition is next (i.e. 0-back, 1-back, etc.). In each of the WM blocks of 30 s duration, only one WM condition is presented (i.e. 2-back), and contains 15 stimuli: three targets and 12 non-targets. Each condition is presented three times. Performance data were recorded during scanning. The dependent variable is accuracy (percentage of correctly identified targets) and commission errors (false alarms). Performance data analysis A repeated-measures ANOVA within patients was conducted on the main performance measures of omission and commission errors, with drug condition (placebo, fluoxetine) and WM load (1-back, 2-back, 3-back) as within-subject factors and group as the betweensubjects factor. For case–control comparisons, two repeated-measures ANOVAs (controls versus ADHD and ASD under placebo/under fluoxetine) were conducted with WM load as the within-subjects factor and group as the between-subjects factor. fMRI image acquisition Gradient-echo echo planar imaging (EPI) MR data were acquired on a GE Signa 3T Horizon HDx system (GE Healthcare, UK) at the Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, UK. A semi-automated quality control procedure ensured consistent image quality. A quadrature birdcage headcoil was used for RF transmission and reception. In each of the 39 non-contiguous planes parallel to the anterior–posterior commissure, 186 T2*-weighted MR images depicting blood oxygen level-dependent (BOLD) contrast covering the whole brain were acquired, with echo time (TE) = 30 ms, repetition time (TR) = 2 s, flip angle = 75°, in-plane voxel size = 3 mm, slice thickness = 3.5 mm, slice skip = 0.5 mm. A whole-brain high-resolution structural scan (inversion recovery gradient echo planar image) on which to superimpose the individual activation maps was also acquired in the intercommissural plane with TE = 30 ms, TR = 3 s, flip angle = 90°, 43 slices, slice thickness = 3.0 mm, slice skip = 0.3 mm, in-plane voxel size = 1.875 mm. This EPI dataset provided complete brain coverage.

fMRI image analysis Blocked designed fMRI data were acquired in randomized block presentation and analysed using the XBAM software package (www.brainmap.co.uk; Brammer et al. 1997), which makes no normality assumptions (often violated in fMRI data), but instead uses median statistics to control outlier effects and permutation rather than normal theory-based inference (Thirion et al. 2007). The fMRI analyses methods for individual and group analyses are described in detail elsewhere (Brammer et al. 1997; |Bullmore et al. 1999a, b, 2001; Cubillo et al. 2014) and in the online Supplementary material. Individual- and group-level analyses for each of the three contrasts (i.e. 1-back/2-back/3-back versus 0-back) are described in more detail elsewhere (Brammer et al. 1997; Bullmore et al. 1999a, b, 2001; Cubillo et al. 2014) and in the online Supplementary material. In brief, the fMRI data were realigned to minimize motion-related artefacts and smoothed using a 7.2-mm full-width at half-maximum (FWHM) Gaussian filter (Bullmore et al. 1999a). Time-series analysis of individual subject activation was performed with a wavelet-based resampling method described previously (Bullmore et al. 2001). We convolved the task epoch of the contrasts of interest (i.e. 3-back–0-back; 2-back–0-back; 1-back–0-back) with two Poisson model functions (delays of 4 and 8 s). Individual activation maps were recalculated by testing the goodness of fit of this convolution with the BOLD time series; the goodness-of-fit calculation used the ratio of the sum of squares (SSQ) of deviations from the mean intensity value due to the model (fitted time series) divided by the sum of squares due to the residuals (original time series minus model time series). This statistic, the SSQ ratio, was used in further analyses. Using rigid body and affine transformation, the individual maps were registered into Talairach standard space (Talairach & Tournoux, 1988). A group brain activation map was then produced for each medication and each experimental condition (see online Supplementary material). Investigation of group by WM load interaction To test for group by WM load interaction effects on brain activation, we conducted a repeated-measures ANCOVA with motion measures as covariates (i.e. rotational and translation movement in Euclidian 3D space), group as the between-subject variable and WM load as the within-subject variable. We conducted randomization-based tests for voxel- or cluster-wise differences as described in detail elsewhere (Brammer et al. 1997; Bullmore et al. 1999a,b, 2001; Cubillo et al.

Fluoxetine and working memory in ADHD and ASD 5 2014) and in the Supplementary material. A significant WM load effect was shown in 12 clusters in the right DLPFC, left DLPFC/IFC, anterior cingulate cortex (ACC)/supplementary motor area (SMA), right IFC/ basal ganglia/thalamus, left basal ganglia/thalamus, thalamus/midbrain, bilateral precentral/postcentral gyri, precuneus/cuneus and bilateral PCC. The BOLD response was extracted for each region and each group. This showed that, for all three groups, activation in the bilateral ACC/SMA, DLPFC, IFC, basal ganglia, thalamus, midbrain and precuneus/cuneus increased progressively with increasing WM load, whereas activation in the precentral/postcentral gyri and PCC was progressively more deactivated with increasing WM load (online Supplementary Fig. S1). However, no group by WM load interaction effects were observed. Consequently, the 3-back–0-back contrast was used in all subsequent analyses, as this contrast was the most challenging WM condition with the highest WM load and elicited the strongest brain activation for all groups. ANCOVA between-group difference analyses For between-group comparisons between controls and patients under either placebo or fluoxetine for the 3-back condition, one-way ANCOVAs with group as factor and motion as covariate were conducted using randomization-based tests for voxel- or clusterwise differences as described in detail previously (Bullmore et al. 1999b, 2001). For these betweengroup comparisons, less than one false-positive activated cluster was expected at p < 0.05 for voxel and p < 0.01 for cluster comparisons. Standardized BOLD response values (SSQ ratios) for each participant were then extracted for the significant clusters of the ANCOVAs, and post-hoc t tests [correcting for multiple comparisons using least significant difference (LSD)] were conducted to identify the direction of the group differences. ANCOVA within-patients interaction effects A 2 × 2 ANCOVA (two medication conditions, two groups) with motion parameters as covariate was conducted using randomized-based testing for voxel- or cluster-wise differences as described previously (Bullmore et al. 2001). Less than one false-positive activation cluster was expected at p < 0.05 at the voxel level and at p < 0.01 at the cluster level. Statistical measures of the BOLD response for each participant were then extracted in each of the significant clusters and post-hoc t tests (correcting for multiple comparisons with LSD) were conducted to identify the direction of the interaction effects.

Normalization effects To test for the statistical significance of any apparent normalization effects of fluoxetine on case–control activation differences observed under placebo, repeatedmeasures t tests were used on the extracted BOLD responses during each medication condition for each of the significant difference clusters in the comparison between controls and patients during placebo. This test was only conducted within patients, given that controls were only tested once, and hence was constant across comparisons. Correlations with clinical behaviour, performance and IQ To test whether clusters that showed group or group by drug interaction effects were related to IQ, performance or clinical behaviour, the BOLD response in these clusters was extracted for each subject and Pearson correlations were performed with IQ and performance (omission and commission errors) in each group, with SDQ Hyperactive/Inattentive and CPRS-R scores in the ADHD group, and with the ADOS Social and Communication and ADOS Stereotyped and Repetitive Behaviours subscales in the ASD group. Results Participant characteristics ANOVAs showed no significant group differences in age, but significant group differences were seen in IQ (F2,55 = 15, p < 0.0001), which was significantly lower in ADHD relative to control and ASD boys (p < 0.0001), who did not differ from each other (see Table 1). ADHD children have typically lower IQ than their healthy peers (Bridgett & Walker, 2006). Therefore, IQ was not covaried because covarying for a measure that is associated with the condition, and hence differs between groups that were not randomly selected, would violate ANCOVA assumptions (Dennis et al. 2009). Furthermore, WM is included in the Wechsler Intelligence Scale for Children, 4th edition (WISC-IV; Wechsler, 2004). Covarying for IQ would therefore also covary for the function of interest (Conway et al. 2003). However, to assess potential effects of IQ on group difference findings, these were correlated with IQ. Performance data Across all subjects there was a significant linear WM load effect on accuracy (F2,53 = 49, p < 0.0001) and commission errors (F2,53 = 104, p < 0.0001) under both placebo and fluoxetine (accuracy: F2,53 = 35, p < 0.0001; commission errors: F2,53 = 20, p < 0.0001).

6 K. Chantiluke et al. Table 2. Performance measures for the N-back WM task for healthy controls, ADHD and ASD boys Performance measures

Controls

ADHD placebo

ADHD fluoxetine

ASD placebo

ASD fluoxetine

Accuracy 1-back Accuracy 2-back Accuracy 3-back Commission 1-back Commission 2-back Commission 3-back

96 (9) 91 (11) 70 (18) 1 (3) 1 (3) 10 (10)

95 (7) 74 (24) 65 (19) 0.6 (3) 6 (12) 14 (16)

94 (10) 84 (15) 68 (28) 0 (0) 6 (7) 11 (14)

99 (4) 88 (17) 75 (24) 1 (4) 2 (4) 10 (12)

98 (6) 95 (10) 78 (19) 1 (4) 1 (3) 8 (16)

WM, Working memory; ADHD, attention deficit hyperactivity disorder; ASD, autism spectrum disorder. Values are given as mean (standard deviation).

Accuracy and commission errors were lowest and highest in the more difficult conditions respectively. No group or drug by group interaction effects were observed. Within patients, repeated-measures ANOVA showed a trend for a drug effect for accuracy (F1,32 = 3, p < 0.1) because both patient groups showed more accuracy under fluoxetine than under placebo (p < 0.08). No other effects were significant (Table 2). fMRI data Movement Repeated-measures ANOVAs using group as the independent factor and maximum x, y, z rotation and translation as repeated measures showed that there were no significant group by movement interaction effects in rotation (F4,106 = 1, p = N.S.) or translation (F4,106 = 2, p = N.S.). Nevertheless, to eliminate potential effects of even small, non-significant motion variance, the two motion parameters of rotation and translation in 3D Euclidean space were used as a covariate in the fMRI analyses. Group brain activation maps Findings are reported in the online Supplementary material and Supplementary Figs S2 and S3. Between-group differences between controls and patients under placebo for 3-back ANCOVA between controls and patients on placebo for the 3-back condition showed a significant group effect in the right DLPFC and the PCC (Fig. 1a, Table 3). Post-hoc analyses showed that the right DLPFC was significantly decreased in activation in the ASD (p < 0.0001) and ADHD (p < 0.05) groups relative to controls, and trend-wise in ASD compared to ADHD boys (p < 0.08). In the PCC, the ASD group had significantly increased deactivation compared to

both controls (p < 0.0001) and ADHD participants (p < 0.005), both of which also deactivated this cluster but did not differ from each other (Fig. 1a, Table 3). Right DLPFC activation in the ASD group (which was reduced relative to controls) was negatively correlated with commission errors (r = −0.6, p < 0.05). No other significant correlations were observed.

Between-group differences between controls and patients under fluoxetine for 3-back ANCOVA between controls and patients on fluoxetine showed a significant group effect in the PCC, as observed under placebo, but not in the DLPFC (Fig. 1b, Table 3). Post-hoc analyses revealed that both ADHD (p < 0.0001) and ASD boys (p < 0.008) deactivated the PCC more than controls. Repeated-measures t tests to test for significant normalization showed that fluoxetine relative to placebo significantly increased activation in right DLPFC in the ASD group only (p < 0.007), whereas in the ADHD group this was not significant. However, fluoxetine relative to placebo significantly increased the deactivation of the PCC in ADHD (p < 0.05) whereas PCC activation changes were not significant for ASD. Correlations with behaviour showed that there was a significant positive correlation between PCC activation and scores on the Hyperactive/Inattentive subscale of the SDQ in the ADHD group (r = 0.6, p < 0.05) and PCC activation and scores on the Social and Communication subscale of the ADOS in the ASD group (r = 0.6, p < 0.05), suggesting that less deactivation of the PCC is associated with more severe ADHD and ASD symptoms. The findings suggest that fluoxetine is more efficacious in the deactivation of the PCC, an area of the DMN, in boys who show less severe forms of ADHD and ASD. No significant correlations between brain activation and IQ were observed.

Fluoxetine and working memory in ADHD and ASD 7 area (BA): 31/7] due to fluoxetine increasing deactivation of this area in the ADHD group and attenuating its deactivation in the ASD group (p < 0.005) (Fig. 1c). No significant correlations between brain activation and behaviour or IQ were observed. Discussion

Fig. 1. Between-group and within-patient comparisons. Axial sections show the between-group ANCOVA findings between controls and patients under (a) placebo and (b) fluoxetine, and (c) the group by medication interaction effects within the two patient groups. Also shown are the statistical measures of the blood oxygen level-dependent (BOLD) response for (a, b) each of the three groups for each of the brain regions that showed a significant group effect and (c) each of the brain regions that showed a significant group by medication interaction effect within patients. ADHD, Attention deficit hyperactivity disorder; ASD, autism spectrum disorder; R, right. Talairach z coordinates are indicated for slice distance (in mm) from the intercommissural line. The right side of the image corresponds to the right side of the brain.

Within-patients group by medication interaction effects ANCOVA showed a significant group by medication interaction effect in the PCC [74 voxels, peak Talairach coordinates (x, y, z): 11, − 30, 37; Brodmann

Under placebo, while performing a WM task, ADHD and ASD boys shared underactivation relative to controls in the right DLPFC, a key area for WM (Wager & Smith, 2003), which at trend level was more severe in ASD relative to ADHD. Furthermore, ASD boys showed disorder-specific increased deactivation of the PCC compared to controls and ADHD boys. Fluoxetine, at a trend level, improved performance in both disorders relative to placebo, but had a disorderdissociated effect on task-positive and task-negative activations. Fluoxetine significantly normalized the right DLPFC underactivation in ASD whereas it only non-significantly ameliorated it in ADHD. However, fluoxetine did significantly increase the deactivation of the PCC DMN region in ADHD boys while decreasing it in ASD. Thus, we show shared deficits in both disorders (DLPFC underactivation), albeit more pronounced in ASD, and also disorder-specific differences (deactivation of the PCC in ASD). Furthermore, we show disorder-dissociated drug effects on task-positive and task-negative activations, with fluoxetine normalizing task-positive right DLPFC underactivation in ASD but enhancing task-negative PCC deactivation in ADHD, both of which may have contributed to the trend for improving WM performance. The shared underactivation relative to controls in the right DLPFC, a key area of manipulation of information during WM (Wager & Smith, 2003), during a verbal N-back task replicates previous findings of right DLPFC underactivation in ADHD children during a verbal N-back task (Cubillo et al. 2014) and in an fMRI meta-analysis of attention tasks (Hart et al. 2013). The finding shows for the first time that DLPFC underactivation in adult ASD during a verbal N-back task (Koshino et al. 2005) is also observed in children with ASD and, furthermore, that this is shared with ADHD and is trendwise even more impaired. The finding of shared right DLPFC deficits in ADHD and ASD extends prior evidence for shared reduction in left DLPFC activation during a parametric sustained attention task (Christakou et al. 2013) to the right-hemispheric homologous area, suggesting that DLPFC dysfunction during attention tasks may be shared between the two disorders and mediate the cognitive overlap of attention and WM problems (Rommelse et al. 2011). The disorder-specific increased deactivation of the PCC, a key region of the DMN, in ASD boys compared

8 K. Chantiluke et al. Table 3. Brain activation differences between controls and patients on placebo or fluoxetine

Post-hoc group differences Placebo ADHD, ASD < C ASD < C, ADHD Fluoxetine ASD, ADHD < C

Brain regions of activation differences

BA

Talairach coordinates (x, y, z)

Voxels

Cluster p value

R DLPFC L PCC/precuneus

9/8 31/7

29, 33, 26 −4, −33, 37

36 70

0.007 0.0006

L PCC/precuneus

31/7

−11, −37, 37

49

0.003

BA, Brodmann area; R, right; L, left, DLPFC, dorsolateral prefrontal cortex; PCC, posterior cingulate cortex.

to control and ADHD boys may have been a compensation for their DLPFC underactivation, given that deactivation of the DMN is typically anti-correlated with task-positive activation in the DLPFC (Christakou et al. 2013) and associated with better task performance (Northoff et al. 2010). Under fluoxetine, right DLPFC underactivation appeared normalized in both groups, but rigorous normalization testing showed significance only for ASD. However, fluoxetine led to an increased deactivation of the PCC in ADHD that, while not different from controls under placebo, was now significantly more pronounced relative to controls under fluoxetine. This disorder-differential effect of fluoxetine on a taskpositive area in ASD (the DLPFC) and a task-negative DMN area in ADHD (the PCC) suggests that fluoxetine had a positive effect on performance and brain activation in both disorders but through different underlying mechanisms, by decreasing task-unrelated self-referential thinking processes mediated by the DMN in ADHD, which has been shown to interfere with task-positive activation and lead to poor inattention in the disorder (Christakou et al. 2013), and by upregulating and normalizing a key task-relevant prefrontal area of WM in ASD. The disorder-specific normalization effects on DLPFC activation and the inverse upregulation/downregulation effects on the PCC in ADHD/ASD respectively may be due to differences in the underlying biochemical abnormalities in the two disorders. Approximately 30% of individuals with ASD have enhanced platelet 5-HT levels (Hranilovic et al. 2007) and reduced binding to the serotonin reuptake transporter and 5-HT2A transporter in the frontal lobe and PCC, which have been linked to poor social communication (Murphy et al. 2006; Nakamura et al. 2010). Hyperserotonaemia may hence be an adaptation to counteract poor 5-HT receptor binding. The increase in 5-HT with fluoxetine may have increased ligand– receptor binding sufficiently to enhance activation in areas where 5-HT receptor density is typically low

(Murphy et al. 2006; Nakamura et al. 2010). This extends prior evidence that chronic fluoxetine can increase metabolic activity in prefrontal areas in a small group of adults with ASD, which was associated with an improvement in ASD behaviour (Buchsbaum et al. 2001). Conversely, in ADHD there is evidence for lower platelet 5-HT levels compared to controls (Spivak et al. 1999) and biochemical serotonergic abnormalities (Oades, 2007, 2008, 2010). Furthermore, serotonergic genotypes are implicated in ADHD and in responsiveness to stimulant medication (Gizer et al. 2009; Sonuga-Barke et al. 2011). Therefore, the increase in 5-HT induced by fluoxetine may not have been sufficient to normalize their DLPFC deficit relative to controls. The increased deactivation of the PCC in ADHD under fluoxetine may be due to the regulatory role of 5-HT as 5-HT1A autoreceptors and heteroreceptors on gamma-aminobutyric acid (GABA)/glutamate neurons have been shown to modulate PCC deactivation in healthy controls during resting-state fMRI, with lower levels of 5-HT1A autoreceptor binding/ more serotonin release being associated with increased deactivation of the PCC (Hahn et al. 2012). Despite significant normalization effects on brain activation, fluoxetine only improved WM performance at a trend level in patients relative to placebo. Practice effects in patients may have overshadowed performance effects, as controls were only tested once. In addition, brain function is more sensitive to pharmacological manipulations than WM performance (Allen et al. 2006; Rose et al. 2006; Cubillo et al. 2014), which could be due to insufficient statistical power for neuropsychological but not fMRI effects (Thirion et al. 2007) in a sample of 17 subjects. The strengths of this study lie in the carefully selected and non-co-morbid patient groups with ADHD and with ASD who were free of psychiatric co-morbidities and, in the case of the ASD group, were medication naïve. A limitation is that, for ethical reasons, the control group was only scanned once whereas patients

Fluoxetine and working memory in ADHD and ASD 9 were scanned twice. The significantly lower IQ in the ADHD group is another limitation. However, low IQ is intrinsically associated with ADHD (Bridgett & Walker, 2006) and it is therefore difficult to separate IQ and diagnosis effects. Moreover, brain activation did not correlate with IQ, suggesting that IQ did not to play a key role. The relatively high IQ of our ASD sample also reduces the generalizability of the findings. In this study we were interested in acute effects of fluoxetine to control for chronic effects on behavioural improvement. However, future studies of chronic fluoxetine administration in ADHD and ASD are essential to assess the clinical relevance of these findings. To summarize, ADHD and ASD patients shared underactivation in the right DLPFC, a key area for WM, with ASD patients showing more pronounced deactivation of the PCC relative to both groups. Fluoxetine had a disorder-dissociated region-specific effect of significantly normalizing the task-positive DLPFC dysfunction in ASD and enhancing task-negative deactivation in the PCC in ADHD, both of which were concomitant with a trend-level improved WM task performance relative to placebo. The region-specific disorder-dissociated effects of fluoxetine may be due to differing biochemical abnormalities underlying the two disorders. Supplementary material For supplementary material accompanying this paper visit http://dx.doi.org/10.1017/S0033291714002232. Acknowledgements This study was supported by the UK Department of Health through the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) for Mental Health at the South London and the Maudsley National Health Service (NHS) Foundation Trust and the Institute of Psychiatry, King’s College London. Declaration of Interest K.R. has received funding from Lilly for another project and consultancy fees from Lilly, Shire and Novartis. The other authors have no conflicts of interest to declare. References Allen PP, Cleare AJ, Lee F, Fusar-Poli P, Tunstall N, Fu CH, Brammer MJ, McGuire PK (2006). Effect of acute tryptophan depletion on pre-frontal engagement. Psychopharmacology 187, 486–497. APA (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Publishing, Washington, DC.

Baddeley A (1996). The fractionation of working memory. Proceedings of the National Academy of Sciences USA 93, 13468–13472. Baddeley A (2003). Working memory: looking back and looking forward. Nature Reviews Neuroscience 4, 829–839. Barrickman L, Noyes R, Kuperman S, Schumacher E, Verda M (1991). Treatment of ADHD with fluoxetine: a preliminary trial. Journal of the American Academy of Child and Adolescent Psychiatry 30, 762–767. Brammer MJ, Bullmore ET, Simmons A, Williams SCR, Grasby PM, Howard RJ, Woodruff PW, Rabe-Hesketh S (1997). Generic brain activation mapping in functional magnetic resonance imaging: a nonparametric approach. Magnetic Resonance Imaging 15, 763–770. Bridgett DJ, Walker ME (2006). Intellectual functioning in adults with ADHD: a meta-analytic examination of full scale IQ differences between adults with and without ADHD. Psychological Assessment 18, 1–14. Buchsbaum MS, Hollander E, Haznedar MM, Tang C, Spiegel-Cohen J, Wei TC, Solimando A, Buchsbaum BR, Robins D, Bienstock C, Cartwright C, Mosovich S (2001). Effect of fluoxetine on regional cerebral metabolism in autistic spectrum disorders: a pilot study. International Journal of Neuropsychopharmacology 4, 119–125. Bullmore E, Brammer M, Rabe-Hesketh S, Curtis V, Morris R, Williams S, Sharma T, McGuire P (1999a). Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fMRI. Human Brain Mapping 7, 38–48. Bullmore E, Long C, Suckling J, Fadili J, Calvert G, Zelaya F, Carpenter TA, Brammer M (2001). Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains. Human Brain Mapping 12, 61–78. Bullmore ET, Suckling J, Overmeyer S, Rabe-Hesketh S, Taylor E, Brammer MJ (1999b). Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Transactions on Medical Imaging 18, 32–42. Carrasco M, Volkmar FR, Bloch MH (2012). Pharmacologic treatment of repetitive behaviors in autism spectrum disorders: evidence of publication bias. Pediatrics 129, 1301–1310. Christakou A, Murphy CM, Chantiluke K, Cubillo AI, Smith AB, Giampietro V, Daly E, Ecker C, Robertson D, Murphy DG, Rubia K (2013). Disorder-specific functional abnormalities during sustained attention in youth with attention deficit hyperactivity disorder (ADHD) and with autism. Molecular Psychiatry 18, 236–244. Conners CK, Sitarenios G, Parker JDA, Epstein JN (1998). The revised Conners’ Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity. Journal of Abnormal Child Psychology 26, 257–268. Conway AR, Kane MJ, Engle RW (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences 7, 547–552. Corbett BA, Constantine LJ, Hendren R, Rocke D, Ozonoff S (2009). Examining executive functioning in children with autism spectrum disorder, attention deficit hyperactivity

10 K. Chantiluke et al. disorder and typical development. Psychiatry Research 166, 210–222. Cubillo A, Smith A, Barrett N, Giampietro V, Brammer M, Simmons A, Rubia K (2014). Drug-specific laterality effects on frontal lobe activation of atomoxetine and methylphenidate in attention deficit hyperactivity disorder boys during working memory. Psychological Medicine 44, 633–646. Cui J, Gao D, Chen Y, Zou X, Wang Y (2010). Working memory in early-school-age children with Asperger’s syndrome. Journal of Autism and Developmental Disorders 40, 958–967. Dalley JW, Roiser JP (2012). Dopamine, serotonin and impulsivity. Neuroscience 215, 42–58. Dennis M, Francis DJ, Cirino PT, Schachar R, Barnes MA, Fletcher JM (2009). Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. Journal of the International Neuropsychological Society 15, 331–343. Gainetdinov RR, Wetsel WC, Jones SR, , Levin ED, Jaber M, Caron MG (1999). Role of serotonin in the paradoxical calming effect of psychostimulants on hyperactivity. Science 283, 397–401. Gammon GD, Brown TE (1993). Fluoxetine and methylphenidate in combination for treatment of attention deficit disorder and comorbid depressive disorder. Journal of Child and Adolescent Psychopharmacology 3, 1–10. Ginestet CE, Simmons A (2011). Statistical parametric network analysis of functional connectivity dynamics during a working memory task. NeuroImage 55, 688–704. Gizer IR, Ficks C, Waldman ID (2009). Candidate gene studies of ADHD: a meta-analytic review. Human Genetics 126, 51–90. Goldberg D, Murray R (eds) (2002). The Maudsley Handbook of Practical Psychiatry. Oxford Medical Publications: Oxford. Goodman R, Scott S (1999). Comparing the Strengths and Difficulties Questionnaire and the Child Behavior Checklist: is small beautiful? Journal of Abnormal Child Psychology 27, 17–24. Hahn A, Wadsak W, Windischberger C, Baldinger P, Höflich AS, Losak J, Nics L, Philippe C, Kranz GS, Kraus C, Mitterhauser M, Karanikas G, Kasper S, Lanzenberger R (2012). Differential modulation of the default mode network via serotonin-1A receptors. Proceedings of the National Academy of Sciences USA 109, 2619–2624. Hart H, Radua J, Nakao T, Mataix-Cols D, Rubia K (2013). Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/ hyperactivity disorder: exploring task-specific, stimulant medication, and age effects. Journal of the American Medical Association Psychiatry 70, 185–198. Hollander E, Soorya L, Chaplin W, Anagnostou E, Taylor BP, Ferretti CJ, Wasserman S, Swanson E, Settipani C (2012). A double-blind placebo-controlled trial of fluoxetine for repetitive behaviors and global severity in adult autism spectrum disorders. American Journal of Psychiatry 169, 292– 299. Hranilovic D, Bujas-Petkovic Z, Vragovic R, Vuk T, Hock K, Jernej B (2007). Hyperserotonemia in adults with autistic disorder. Journal of Autism and Developmental Disorders 37, 1934–1940.

Kasper LJ, Alderson RM, Hudec KL (2012). Moderators of working memory deficits in children with attention-deficit/ hyperactivity disorder (ADHD): a meta-analytic review. Clinical Psychology Review 32, 605–617. King BH, Hollander E, Sikich L, McCracken JT, Scahill L, Bregman JD, Donnelly CL, Anagnostou E, Dukes K, Sullivan L, Hirtz D, Wagner A, Ritz L, Network SP (2009). Lack of efficacy of citalopram in children with autism spectrum disorders and high levels of repetitive behavior: citalopram ineffective in children with autism. Archives of General Psychiatry 66, 583–590. Kobel M, Bechtel N, Weber P, Specht K, Klarhofer M, Scheffler K, Opwis K, Penner IK (2009). Effects of methylphenidate on working memory functioning in children with attention deficit/hyperactivity disorder. European Journal of Paediatric Neurology 13, 516–523. Koshino H, Carpenter PA, Minshew NJ, Cherkassky VL, Keller TA, Just MA (2005). Functional connectivity in an fMRI working memory task in high-functioning autism. NeuroImage 24, 810–821. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, Pickles A, Rutter M (2000). The Autism Diagnostic Observation Schedule Generic: a standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders 30, 205–223. Lord C, Rutter M, Couteur A (1994). Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders 24, 659–685. McGough JJ, McCracken JT, Loo SK, Manganiello M, Leung MC, Tietjens JR, Trinh T, Baweja S, Suddath R, Smalley SL, Hellemann GSugar CA (2009). A candidate gene analysis of methylphenidate response in attentiondeficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry 48, 1155–1164. Murphy DG, Daly E, Schmitz N, Toal F, Murphy K, Curran S, Erlandsson K, Eersels J, Kerwin R, Ell P, Travis M (2006). Cortical serotonin 5-HT2A receptor binding and social communication in adults with Asperger’s syndrome: an in vivo SPECT study. American Journal of Psychiatry 163, 934–936. Nakamura K, Sekine Y, Ouchi Y, Tsujii M, Yoshikawa E, Futatsubashi M, Tsuchiya KJ, Sugihara G, Iwata Y, Suzuki K, Matsuzaki H, Suda S, Sugiyama T, Takei N, Mori N (2010). Brain serotonin and dopamine transporter bindings in adults with high-functioning autism. Archives of General Psychiatry 67, 59–68. Nishizawa S, Benkelfat C, Young S, Leyton M, Mzengeza S, De Montigny C, Blier P, Diksic M (1997). Differences between males and females in rates of serotonin synthesis in human brain. Proceedings of the National Academy of Sciences USA 94, 5308–5313. Northoff G, Qin P, Nakao T (2010). Rest-stimulus interaction in the brain: a review. Trends in Neurosciences 33, 277–284. Oades RD (2007). Role of the serotonin system in ADHD: treatment implications. Expert Review of Neurotherapeutics 7, 1357–1374.

Fluoxetine and working memory in ADHD and ASD 11 Oades RD (2008). Dopamine-serotonin interactions in attention-deficit hyperactivity disorder (ADHD). Progress in Brain Research 172, 543–565. Oades RD (2010). The role of serotonin in attention-deficit hyperactivity disorder (ADHD). Handbook of Behavioral Neuroscience 21, 565–584. Oldfield RC (1971). The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113. Quintana H, Butterbaugh GJ, Purnell W, Layman AK (2007). Fluoxetine monotherapy in attention-deficit/hyperactivity disorder and comorbid non-bipolar mood disorders in children and adolescents. Child Psychiatry and Human Development 37, 241–253. Rommelse NNJ, Geurts HM, Franke B, Buitelaar JK, Hartman CA (2011). A review on cognitive and brain endophenotypes that may be common in autism spectrum disorder and attention-deficit/hyperactivity disorder and facilitate the search for pleiotropic genes. Neuroscience and Biobehavioral Reviews 35, 1363–1396. Rose EJ, Simonotto E, Spencer EP, Ebmeier KP (2006). The effects of escitalopram on working memory and brain activity in healthy adults during performance of the n-back task. Psychopharmacology 185, 339–347. Rutter M, Bailey A, Lord C (2003). The Social Communication Questionnaire: Manual. Western Psychological Services: Los Angeles, CA. Sonuga-Barke EJ, Kumsta R, Schlotz W, Lasky-Su J, Marco R, Miranda A, Mulas F, Oades RD, Banaschewski T, Mueller U (2011). A functional variant of the serotonin transporter gene SLC6A4 moderates impulsive choice in attention-deficit/hyperactivity disorder boys and siblings. Biological Psychiatry 70, 230–236. Spivak B, Vered Y, Yoran-Hegesh R, Averbuch E, Mester R, Graf E, Weizman A (1999). Circulatory levels of catecholamines, serotonin and lipids in attention deficit

hyperactivity disorder. Acta Psychiatrica Scandinavica 99, 300–304. Talairach J, Tournoux P (1988). Co-planar Stereotaxic Atlas of the Human Brain. Thieme Medical Publishers: New York. Thirion B, Pinel P, Meriaux S, Roche A, Dehaene S, Poline JB (2007). Analysis of a large fMRI cohort: statistical and methodological issues for group analyses. NeuroImage 35, 105–120. Wager TD, Smith EE (2003). Neuroimaging studies of working memory: a meta-analysis. Cognitive Affective and Behavioral Neuroscience 3, 255–274. Wechsler D (1999). Wechsler Abbreviated Scale of Intelligence. The Psychological Corporation: San Antonio, TX. Wechsler D (2004). Wechsler Intelligence Scale for Children, Fourth UK Edition. The Psychological Corporation: London. West L, Brunssen SH, Waldrop J (2009). Review of the evidence for treatment of children with autism with selective serotonin reuptake inhibitors. Journal for Specialists in Pediatric Nursing 14, 183–191. WHO (1994). The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. World Health Organization: Geneva. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biological Psychiatry 57, 1336–1346. Williams K, Wheeler DM, Silove N, Hazell P (2010). Selective serotonin reuptake inhibitors (SSRIs) for autism spectrum disorders (ASD). Cochrane Database of Systematic Reviews. Issue 8, Art. No. CD004677. Wong DT, Bymaster FP, Engleman EA (1995). Prozac (fluoxetine, Lilly 110140), the first selective serotonin uptake inhibitor and an antidepressant drug: twenty years since its first publication. Life Sciences 57, 411–441.