ADHD- and medication-related brain activation effects in concordantly affected parent child dyads with ADHD

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1 Journal of Child Psychology and Psychiatry 48:9 (2007), pp doi: /j x ADHD- and medication-related brain activation effects in concordantly affected parent child dyads with ADHD Jeffery N. Epstein, 1 B.J. Casey, 2 Simon T. Tonev, 3 Matthew C. Davidson, 2 Allan L. Reiss, 4 Amy Garrett, 4 Stephen P. Hinshaw, 5 Laurence L. Greenhill, 6 Gary Glover, 4 Keith M. Shafritz, 7 Alan Vitolo, 5 Lisa A. Kotler, 6 Matthew A. Jarrett, 3 and Julie Spicer 2 1 Department of Pediatrics, Cincinnati Children s Hospital Medical Center, Cincinnati, OH, USA; 2 Sackler Institute, Weill Medical College of Cornell University, New York, USA; 3 Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA; 4 Department of Psychiatry, Stanford University Medical Center, Palo Alto, CA, USA; 5 Department of Psychology, University of California, Berkeley, CA, USA; 6 Division of Child Psychiatry, Columbia University Medical Center/New York State Psychiatric Institute, New York, USA; 7 Department of Psychology, Hofstra University, Hempstead, NY, USA Background: Several studies have documented fronto-striatal dysfunction in children and adolescents with attention deficit/hyperactivity disorder (ADHD) using response inhibition tasks. Our objective was to examine functional brain abnormalities among youths and adults with ADHD and to examine the relations between these neurobiological abnormalities and response to stimulant medication. Method: A group of concordantly diagnosed ADHD parent child dyads was compared to a matched sample of normal parent child dyads. In addition, ADHD dyads were administered doubleblind methylphenidate and placebo in a counterbalanced fashion over two consecutive days of testing. Frontostriatal function was measured using functional magnetic resonance imaging (fmri) during performance of a go/no-go task. Results: Youths and adults with ADHD showed attenuated activity in fronto-striatal regions. In addition, adults with ADHD appeared to activate non-fronto-striatal regions more than normals. A stimulant medication trial showed that among youths, stimulant medication increased activation in fronto-striatal and cerebellar regions. In adults with ADHD, increases in activation were observed in the striatum and cerebellum, but not in prefrontal regions. Conclusions: This study extends findings of fronto-striatal dysfunction to adults with ADHD and highlights the importance of frontostriatal and frontocerebellar circuitry in this disorder, providing evidence of an endophenotype for examining the genetics of ADHD. Keywords: ADHD, adolescence, adulthood, brain imaging, development, fmri, methylphenidate, neuropsychology, children, parents. Abbreviations: MTA: Multimodal Treatment Study of ADHD; LNCG: local normative comparison group; CAADID: Conners Adult ADHD Diagnostic Interview for DSM-IV. Attention-deficit/hyperactivity disorder (ADHD) is a common developmental disorder of childhood with a prevalence rate of approximately 5 8% in the US (American Psychiatric Association, 1994; Centers for Disease Control, 2005). ADHD and related impairments frequently persist into adolescence and adulthood (Biederman, Mick, & Faraone, 2000; Rasmussen & Gillberg, 2000; Weiss & Hechtman, 1993). Beyond the defining behavioral characteristics of ADHD (i.e., inattention, impulsivity, and hyperactivity), a large neuropsychological literature has documented several areas of cognitive impairment. Most notably, deficits in response inhibition (i.e., suppression of a prepotent response in favor of an appropriate response; Nigg, 2001) have been reported for children, adolescents, and adults with ADHD using a variety of neuropsychological tasks Conflict of interest statement: No conflicts declared. (Hervey, Epstein, & Curry, 2004; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Deficits in response inhibition are hypothesized to result from some form of abnormality in the frontostriatal circuitry in the brains of ADHD patients. Evidence for this hypothesis comes from several sources. First, imaging studies with normal participants clearly demonstrate the role of the prefrontal cortices in tasks involving response inhibition (Casey et al., 1997b; Konishi et al., 1999; Konishi, Nakajima, Uchida, Sekihara, & Miyashita, 1998; Menon, Adleman, White, Glover, & Reiss, 2001; Rubia et al., 2001; Tamm, Menon, & Reiss, 2002). Second, patients with frontal lobe lesions have response inhibition deficits similar to those seen in patients with ADHD (Stuss, Murphy, Binns, & Alexander, 2003). Third, several studies have shown correlations between behavioral performance on response inhibition tasks and MRI-based measures of Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

2 900 Jeffery N. Epstein et al. prefrontal and striatal regions (Casey et al., 1997a; Hill et al., 2003; Rubia, 2002). Taken together, considerable evidence supports fronto-striatal network abnormalities as contributing to observed behavioral deficits in response inhibition in ADHD. Several functional imaging studies have examined differences in brain activity between ADHD and normal participants on response inhibition tasks (Durston et al., 2003; Pliszka et al., 2006; Rubia et al., 1999; Schulz et al., 2004; Smith, Taylor, Brammer, Toone, & Rubia, 2006; Tamm, Menon, Ringel, & Reiss, 2004; Vaidya et al., 1998). A number of these studies have shown lower levels of striatal activation (e.g., caudate nucleus) among children with ADHD relative to controls (Booth et al., 2005; Durston et al., 2003; Rubia et al., 1999; Vaidya et al., 1998, 2005). The majority of studies have found less prefrontal activation in children with ADHD (Booth et al., 2005; Rubia et al., 1999; Rubia, Smith, Brammer, Toone, & Taylor, 2005; Smith et al., 2006; Vaidya et al., 2005), although at least one study has reported the opposite (Durston et al., 2003; Schulz et al., 2004). In those investigations showing less prefrontal activity in ADHD, the pattern has been isolated predominantly within the inferior frontal gyri (IFG; Booth et al., 2005; Durston, Mulder, Casey, Ziermans, & van Engeland, in press; Rubia et al., 1999; Rubia et al., 2005). This pattern of decreased IFG activation has been shown in both children and adolescents with ADHD and has recently been reported in medication-naïve children with ADHD on a mental rotation task (Silk et al., 2005). On the other hand, patterns of activation in regions such as the anterior cingulate cortex have been mixed, with some groups reporting greater activity (Pliszka et al., 2006; Schulz et al., 2004) and others showing less activity (Bush et al., 1999; Rubia et al., 1999; Tamm et al., 2004) in ADHD samples. In addition to examining brain activation differences between patients with ADHD and normal research participants, the effects of stimulant medications have also been examined using brain imaging. A review of previous imaging studies examining the effects of MPH on brain function yields mixed results across adult and pediatric samples. In adults with ADHD, MPH has been associated with increased activation in the cerebellar vermis (Schweitzer et al., 2003), right thalamus (Schweitzer et al., 2004), and precentral gyrus (Schweitzer et al., 2004), and decreased activation in the prefrontal cortex (Schweitzer et al., 2004), precentral gyri (Schweitzer et al., 2003), right claustrum (Schweitzer et al., 2003), and striatum (Matochik, 1994; Schweitzer et al., 2003). These studies have primarily used working memory tasks. In pediatric samples, the pattern of results is somewhat different. Vaidya et al. (1998) found that stimulants increased frontal activation on a response inhibition task in children with ADHD. MPH-related increases in activation in the cerebellar vermis (Anderson, Polcari, Lowen, Renshaw, & Teicher, 2002) and striatum (Shafritz, Marchione, Gore, Shaywitz, & Shaywitz, 2004; Vaidya et al., 1998) have also been noted. However, others have reported MPH-related decreases in striatal activity (Lee et al., 2005). Inconsistencies within the literature and across developmental stages may result from multiple factors. One reason is the broad range of imaging methods (e.g., positron emission tomography (PET), functional magnetic resonance imaging [fmri]) and behavioral paradigms (e.g., stop signal task, go/nogo task) utilized across studies. In particular, pediatric and adult studies are largely different in that adult studies utilize primarily PET while pediatric studies have utilized fmri. To date, no ADHD imaging study has used the same behavioral paradigms with similar imaging methods across pediatric and adult samples. In addition, the sample composition across studies has been quite heterogeneous. A wide variety of inclusion criteria have been used (e.g., multiple ADHD subtypes vs. a single ADHD subytpe; requiring only past evidence of ADHD symptomatology vs. current ADHD symptomatology; samples with medication history vs. medication-naïve patients). To date, samples have been study-specific, serving to increase heterogeneity within most experimental samples. One method for decreasing sample heterogeneity is to utilize an existing, comprehensively defined, and well-studied sample, which (a) allows for accurate description of the study sample (e.g., comorbid disorders), (b) documents the persistence of ADHD symptomatology over time, and (c) ensures common developmental trajectories among the sample. One such existing study sample includes the youths who participated in the Multimodal Treatment Study of ADHD (MTA; MTA Cooperative Group, 1999). This sample is one of the most well-defined and comprehensively studied pediatric ADHD samples available. Because children with ADHD symptoms can possess a variety of genetic and/or non-genetic risk factors (Biederman, 2005), another method for increasing homogeneity within an ADHD sample is to utilize a family history of ADHD to select study patients. Seidman and colleagues (Seidman et al., 1995; Seidman, Biederman, Faraone, Weber, & Ouellette, 1997) have shown that adolescents with a family history of ADHD have more neuropsychological deficits, particularly on response inhibition measures, than children without such a family history. Selecting participants based on a family history of ADHD has the capability to produce a more biologically-at risk group of ADHD patients, which would serve to increase the power to detect between-group differences. With this method, an innovative study design may be used in which both youths with ADHD and their affected parents are studied. This design

3 ADHD frontostriatal dysfunction 901 affords examination of ADHD-specific functional deficits at two different developmental stages. Behaviorally, symptom patterns change with development (Biederman et al., 2000). Also, brain morphology of key brain regions changes over time (Castellanos et al., 2002). By using both youths and their parents, differences across developmental stages may be qualitatively examined which may further our understanding of the developmental course of ADHD-related brain abnormalities and the possible developmental changes in the brain s response to stimulant medication. In the present study, youths from the MTA study serve as the patient sample, and a matched comparison group of normal youths recruited as part of the MTA study serve as controls. To obtain a twogeneration sample, we selected probands based on the presence of an ADHD diagnosis in both the youth and at least one biological parent. Further, the present study utilizes both youths and parents as study participants, allowing analyses of between-group effects and medication effects at two different developmental stages. Our chief objectives were to examine deficits compared to control subjects and medication-related changes in ADHD-related frontostriatal brain circuitry using fmri. Based on previous studies, we predicted between-group differences in fronto-striatal and frontocerebellar circuits, parietal cortices, and anterior cingulate for the ADHD parent child dyads compared to the control dyads. Stimulant medication was predicted to produce brain activation changes especially in the striatal region. Overall study design Sample An initial sample of twenty ADHD youth parent dyads were recruited from 3 of the 7 geographical recruiting sites for the MTA study (i.e., Duke University Medical Center, University of California at Berkeley, and New York State Psychiatric Institute). All children in the MTA study received a diagnosis of ADHD, Combined Type at the time of study recruitment when children were 7 9 years of age (see MTA Cooperative Group, 1999 for MTA study methods and total sample description). At the time of entry into the present study, a DISC-P (version 4.0; Shaffer, Fisher, Lucas, Dulcan, & Schwab- Stone, 2000) was administered. Youths were required to meet DSM-IV ADHD diagnostic criteria for any ADHD subtype based on the current DISC to be included in the study. Biological parents meeting DSM-IV ADHD criteria using the Conners Adult ADHD Diagnostic Interview for DSM-IV (CAADID; Epstein, Johnson, & Conners, 2001; Epstein & Kollins, 2006) were included in the study. Youths and their parents in both groups were excluded if they had an estimated IQ below 80, suffered from any neurological disease, had a diagnosis of bipolar disorder, psychosis, or pervasive developmental disorder, or had any history of head trauma. For those participants taking stimulant medication, a washout period (5 multiplied by the half-life of the medication) was required. Participants had to be free of neuroleptic medications for 6 months prior to the study. Nine healthy control dyads matched to these ADHD youths age and sex were recruited from a Local Normative Comparison Group (LNCG) that had been recruited for the MTA study (MTA Cooperative Group, 1999). LNCG children were living in the same communities and attending the same schools as the MTA children at baseline assessment. For the purposes of this study, children in the LNCG group had to have fewer than 3 ADHD symptoms within each DSM-IV ADHD symptom domain as assessed by the DISC parent report. In addition, parents in the LNCG group were matched on gender to the ADHD parent and were required to have fewer than 3 symptoms in each DSM-IV ADHD symptom domain as measured by the CAADID. All youths and their parents in the LNCG groups met the same exclusion criteria as those in the MTA group except for diagnostic status. Imaging protocol After description of the study, written informed consent was obtained for all subjects. All participants were acclimated to the scanning environment on the first day of image acquisition using a mock scanner. See Epstein et al. (in press) for a full description of the simulator protocol. Dyads were scanned on consecutive days. Using a counterbalanced and double-blinded design, dyads were assigned to receive either placebo or immediate-release methylphenidate on the first day of image acquisition; dyads received the complementary condition on the second day of image acquisition. Medication dosage was determined using a 0.3 mg/kg formula with a maximum of 20 mg. Using this formula, all of the participants received a 20 mg dosage except for two youths who received a 17.5 mg dose. The dosage taken was not necessarily the optimal dosage for each individual participant nor was it necessarily equivalent to the dosage regularly taken by some participants prior to study entry. Functional imaging using the behavioral paradigm described above began between 60 and 120 minutes after ingestion and ended between 100 and 160 minutes after ingestion. Dyads engaged in the identical behavioral paradigm on both days of image acquisition. The functional images were collected in minutes each session. The study was approved by Institutional Review Boards at each of the participating institutions.

4 902 Jeffery N. Epstein et al. Behavioral paradigm The behavioral paradigm was a go/no-go task. Participants were required to press the response button with the right index finger for each letter that appeared on the screen except for the letter X. The task consisted of five groups (runs) of 128 trials each run. Each run lasted 5 min, 20 sec. Each letter, approximately 2.5 cm in size, appeared for 500 milliseconds with an inter-stimulus interval of 2000 ms. The letter X occurred on approximately 20% of all trials (n ¼ 125), which were presented randomly throughout the run. Other letters were randomly selected from the alphabet. Performance measures on the go/no-go task were mean reaction time and standard deviation for correct go trials, errors of omission, errors of commission, and d-prime. The signal detection measure, d-prime, reflects the subject s perceptual sensitivity to targets; it is the distance between the signal distribution and noise distribution in standard score units. Higher d-prime values indicate higher levels of signal detection relative to noise (i.e., better discrimination between targets and nontargets). Image acquisition and analysis Subjects were scanned with General Electric 1.5 Tesla fmri scanners (General Electrical Medical Systems, Milwaukee, Wisconsin) at Duke University Medical Center, Stanford University School of Medicine and Cornell University Medical College. 1 A whole brain high resolution T1 weighted anatomic scan ( in-plane resolution, 240 mm field of view; 124 slices at 1.5 mm per slice) was acquired for each subject for transformation and localization of functional data into Talairach space. Functional data were collected with a spiral in-andout sequence (Glover & Law, 2001) (TR ¼ 2500 msec, TE ¼ 40 msec, flip angle ¼ 90, FOV ¼ 240 mm, matrix). Each volume contained 33 oblique slices (3.2 mm thick with 1 mm skip) with an in-plane resolution of mm covering the entire brain. Although the time series sampling interval of TR ¼ 2.5 secs was thus equal to the interstimulus presentation interval, signal detection efficiency was not compromised because the average 1 In order to ensure comparability across sites, all sites used identical scanners and software for imaging. Prior to image acquisition, the same individuals were scanned at all sites in order to ensure similar signal to noise and contrast to noise across sites. Further, all sites scanned identical phantoms on a monthly basis to check and guard against any drift during the course of the study. Lastly, all data were normalized during preprocessing to correct for any small signal to noise variations across site. This cross-site methodology was developed and based on a similar multi-site functional imaging study (Casey et al., 1998). hemodynamic response delay of 5 6 secs is wellsampled at this rate. The BrainVoyager QX software package (Brain Innovations, Maastricht, The Netherlands) was used to perform random effects analyses of the functional data. Preprocessing of the functional data involved three-dimensional motion detection and correction (spatial alignment of all volumes to the first volume by rigid transformation) and linear trend removal. Estimated translation and rotation movements were less than 3 mm for all runs used in the analyses. Subjects who did not have at least 3 runs of usable data were excluded from the study. Functional data were co-registered to the anatomic volume by alignment of corresponding points and manual adjustments to obtain registration. Functional data were then transformed into Talairach space with standard landmarks and were interpolated to a resolution of 1 mm. Signal values in each time course were normalized to z-scores representing a change from the mean signal for that run. The signal values for the correct no-go trials were considered to be the effects of interest and were modeled with a convolution of an ideal boxcar response (assuming a value of 1 for the volume of the no-go task presentation and a value of 0 for the remaining time points) with a linear model of the hemodynamic response (Boynton, Engel, Glover, & Heeger, 1996). These predictors were used to build a design matrix for each time course in the experiment. Only correct trials were included in the matrices and subsequent analyses. Hence, correct no-go trials were contrasted with correct go trials to identify response inhibition-related activation patterns which were compared within groups. A whole brain analysis was conducted, but contrasts were examined only in hypothesized ROIs identified below. Three-dimensional statistical maps were generated by assigning an F value to each voxel corresponding to the correct no-go trials and calculated on the basis of the least mean squares solution of the GLM. Contrast analyses were then performed based on t-score differences between the beta weights of this predictor relative to the mean beta weights for that subject. A p level of.05 was used and multiple comparisons were corrected using a contiguity threshold of 5 acquisition-based voxels (Forman et al., 1995). Analyses were conducted separately for youths and adults to examine between-group and medication effects at two different developmental stages. Analyses could not be feasibly conducted combining or comparing these two developmentally different groups due to the number of differences between parents and children in gender, stimulant medication histories, and methods of recruitment across the youths and adult groups (see Table 1) which could result in erroneous interpretations about developmental differences.

5 ADHD frontostriatal dysfunction 903 Table 1 Demographic characteristics ADHD (whole sample) ADHD (matched sample) Control (n ¼ 15) Youths (n ¼ 13) (n ¼ 9) Youths (n ¼ 9) (n ¼ 9) Youths (n ¼ 9) Gender Male (N) Female (N) Ethnicity Caucasian (N) African American (N) Other (N) Age in years Mean (SD) 50.1 (8.1) 17.3 (1.2) 48.6 (9.0) 17.3 (1.2) 46.8 (3.9) 17.4 (1.1) Handedness Right (N) Left (N) Number of each ADHD Subtype Inattentive 6 6* Hyperactive/Impulsive Combined Number with history of N/A N/A stimulant medication Number currently taking N/A N/A stimulant medication Number with specified psychological disorder Eating disorders Mood disorders Obsessive compulsive disorder Posttraumatic stress disorder 1 N/A 1 N/A 0 N/A Social phobia Specific phobia Oppositional defiant disorder N/A 1 N/A 1 N/A 0 Conduct disorder N/A 1 N/A 1 N/A 0 Alcohol use disorders Marijuana use disorders Notes: N/A ¼ not assessed by diagnostic instrument; *One of the children with ADHD did not meet full diagnostic symptom criteria for ADHD. The child had 4 Inattentive symptoms and 0 Hyperactive/Impulsive symptoms. Exclusion of this participant did not alter the study results. Study #1: Between-group effects Methods Subjects. Nine youth parent dyads who were randomly assigned to take placebo on the first day of scanning and the 9 matched LNCG dyads were used to test for between-group differences. ADHD dyads who were randomly assigned to take stimulant medication on the first day of testing were excluded because of the effects of medication on brain function. Demographic data for the matched ADHD and LNCG groups, including gender, ethnicity, age, handedness, diagnostic status, and comorbid disorders, are presented in Table 1. There were no statistically significant differences between the two matched samples (parents or youths) on gender, ethnicity, age, or handedness (all p values >.2). Analyses. Participants were required to perform with greater than 70% accuracy on the behavioral paradigm and have less than a voxel (i.e., less than 3 mm) of head movement for at least three runs to be included in the analyses. The 70% accuracy criterion was derived based on the need to have enough go and no-go trials to obtain a stable hemodynamic response function for each of these trial types. The half voxel of motion criterion is based on Krings et al. (2001). The exclusion of one dyad member excluded the entire dyad from the analyses because of matching criteria. As such, 9 ADHD and 9 normal control dyads (n ¼ 36 subjects) described above met these inclusion criteria. Diagnostic groups were matched on number of runs used in the analysis to prevent differences in statistical power between groups. A whole brain analysis was conducted, but post hoc contrasts were examined in ROIs that have been associated with response inhibition or have been identified in previous imaging studies as being different between ADHD and comparison groups. These areas included the striatum, frontal gyri, anterior cingulate cortex, cerebellum, and posterior parietal gyrus. In ROIs where between-group differences were present, mean beta weights were generated for the

6 904 Jeffery N. Epstein et al. Table 2 T-test results, means and standard deviations for ADHD and LNCG youths and parents on the behavioral task Youths ADHD (n ¼ 9) LNCG (n ¼ 9) Effect size (d) t-tests ADHD (n ¼ 9) LNCG (n ¼ 9) Effect size (d) T-tests Errors of omission 6.44 (8.59) 2.34 (3.03) (8.87) 2.10 (3.30) Errors of commission (14.95) (7.74) (10.56) 8.78 (9.08) Reaction time 439 (69) 352 (30) ** 450 (52) 440 (85) Reaction time SD (56.7) 73.3 (22.5) *** (51.4) (35.9) D-prime 2.70 (1.03) 3.08 (.61) (.99) 3.86 (.52) Note: **p <.01; ***p <.001. ROI using activation maps to define the ROI borders. These beta weights were then correlated with the behavioral measures to examine relations between functional brain activation and performance. Results and discussion Table 2 shows differences between the ADHD and LNCG group s behavioral performance on the go/nogo task conducted in the scanner. Children with ADHD had slower and more variable reaction times (RTs) than children in the LNCG group, but no statistically significant differences were found between parents with ADHD and parents without ADHD. Youths with ADHD showed less brain activation than youths with no history of ADHD during no-go trials in bilateral middle frontal gyrus (Brodmann s areas 9 & 46), right inferior frontal gyrus (Brodmann s areas 45 & 47), right inferior parietal lobule (Brodmann s area 40), anterior cingulate (Brodmann s area 32), and bilateral caudate nucleus (Figure 1). Youths with ADHD did not show higher activation compared to LNCG youths in any pre-identified regions of interest. with ADHD also had less activation than controls in right inferior frontal gyrus (Brodmann s area 45) and left caudate nucleus. Less activation among parents with ADHD vs. controls was also observed in the left inferior frontal gyrus (Brodmann s area 44). On the other hand, parents with ADHD showed more activation in the left inferior parietal lobule (Brodmann s area 40) and anterior cingulate (Brodmann s area 32) than parents without ADHD. 2 See Table 3 for regions of interest, maximum t-value, and Talaraich coordinates. See Tables 6 and 7 for the main effect of condition for each group separately. 3 2 Post hoc correlational analysis showed no within-dyad correlation between levels of activation (i.e., beta values) in youths with levels of activation in their parents (all p values >.05) for any identified region of interest. 3 Whole brain between-group analyses revealed increased right parahippocampal gyrus (BA 20) and decreased left thalamic activation for ADHD youths compared to normal controls. However, these regions did not survive our contiguity threshold. No additional regions of interest were different among parents in whole brain analyses. The relation between behavioral task performance and corresponding brain activation was examined using correlational analyses. Because the trials of interest were no-go trials (i.e., inhibition trials), only errors of commission and d-prime were utilized as performance indicators. Performance on these indicators was correlated with individual beta weights for each individual ROI that was identified in the between-group fmri analyses. Correlations between errors of commission and the various ROIs in parents and children were all non-significant (all p values >.05). However, d-prime was positively correlated with brain activation in the right inferior frontal gyrus in children (r ¼.47, p ¼.048; Brodmann s area 47) and parents (r ¼.66, p ¼.003; Brodmann s area 45). Scatter plots (see Figure 2) of these correlations indicate that the association between right IFG and d-prime was consistent across ADHD and control youths, but was confined to the ADHD participants in the adult sample. D-prime was also positively correlated with left caudate nucleus activation in parents (r ¼.71, p ¼.001). Note that higher values of d-prime indicate better discrimination; hence the more activation in these regions, the better the discrimination. The novel use of concordantly-affected parent child dyads allowed a cross-sectional examination of fronto-striatal abnormalities in manifestations of ADHD at two different ages. We found that youths diagnosed with ADHD showed lower brain activation in fronto-striatal regions on a response inhibition task than comparison youths. of the ADHD sample who themselves met criteria for ADHD showed similar fronto-striatal abnormalities to their children as well as some interesting differences. Consistent with research reporting similar neuropsychological deficits (see reviews by Pennington & Ozonoff, 1996 and Hervey et al., 2004) across childhood and adulthood, these study findings support similar fronto-striatal abnormalities in ADHD patients across youth and adult samples. Further, because the adults and youths in this sample were biologically related, the current pattern of results suggests a possible biologically-based mechanism of transmission (i.e., heritability). Using a family history of ADHD as the basis for inclusion resulted in robust between-group differ-

7 ADHD frontostriatal dysfunction 905 The right side of the image corresponds to the left side of the brain. Figure 1 Diagnosis-based activation differences (control ADHD) during response inhibition for parent and youth participants. For parents, controls show significantly greater activation than ADHD participants in left caudate nucleus and bilaterally in inferior frontal gyrus whereas ADHD participants show greater activation in anterior cingulate and precuneus. For youths, controls show greater activation in anterior cingulate, right inferior frontal gyrus, and bilaterally in caudate nucleus a Table 3 Regions of interest showing significant differences in brain activation between ADHD and normal controls Talairach coordinates Region of interest Brodmann s area x y z Contrast t-value Maximum Volume (mm 3 ) Youth Middle frontal gyrus R 46/ Control>ADHD L 46/9 ) Control>ADHD Inferior frontal gyrus (R) Control>ADHD Anterior cingulate Control>ADHD Inferior frontal gyrus (R) Control>ADHD Caudate nucleus R Control>ADHD L ) Control>ADHD Inferior parietal lobule (R) )43 42 Control>ADHD Anterior cingulate 32 ) ADHD>Control ) Inferior frontal gyrus R Control>ADHD L 44 ) Control>ADHD Caudate nucleus (L) ) Control>ADHD Inferior parietal lobule (L) 40 )47 )67 27 ADHD>Control ) Precuneus 7 0 )70 33 ADHD>Control ) ences in brain activation in our youth sample and also served to identify at least two regions, left caudate nucleus and right inferior frontal gyrus, showing diminished activation among children and parents with ADHD. Right IFG activation was also correlated with performance on the behavioral task. Indeed, the IFG has been repeatedly found to be involved in response inhibition tasks among both control and disordered populations (see review by Aron & Poldrack, 2005). Decreased IFG and caudate activation has also been one of the most consistent ADHD-related findings in fmri studies comparing children with ADHD to normal controls (Booth et al., 2005; Durston et al., 2003; Rubia et al., 1999, 2005; Smith et al., 2006; Vaidya et al., 1998; Vaidya et al., 2005). Also, a recent study report (Sowell et al., 2003) and a review of the structural literature (Swanson, Castellanos, Murias, LaHoste, & Kennedy, 1998) demonstrate significant structural differences in right IFG between children with and without ADHD. Given these past and current findings, IFG functional activation seems to be a candidate endophenotype in examining phenotype-genotype relations in ADHD. Indeed, Durston et al. (in press) have shown a similar concordance in IFG functional

8 906 Jeffery N. Epstein et al. Youths r=.45 r=.82 r=.4 5 r=-.15 Overall r=.47, p<.05 Overall r=.66, p<.01 Figure 2 Correlations (and associated regression lines) between d-prime and right inferior frontal gyrus beta values overall, and as a function of diagnostic group (ADHD, control) for both parent and youth participants abnormalities among children with ADHD and their unaffected siblings. Although some brain regions showed similar patterns of activity across youths with ADHD and their parents, the parents had fewer regional differences from the control parents than did their children with respect to control children. In addition, some brain regions showed higher activation in the parents with ADHD versus the control parents. The different pattern of neurofunctional deficits across youths and adults is quite interesting and raises the question of whether such disparities are evidence of a developmental phenomenon or, rather, an artifact of the experimental methods used for this study. There were several characteristics that differentiated the youth and adult ADHD samples in this study. Most notably, adults were less likely to have received medication for ADHD. Furthermore, parents in the study were recruited based on their child s ADHD status; as a result, the non-referred adult ADHD sample used in this study may have been less severely impaired than the child sample and possibly less severe than adult ADHD samples used in previous studies. There were also differences between the youth and adult samples in terms of gender distribution (more males in youth sample), presence of mood disorders (more mood disorders in the adult sample), history of stimulants (lower usage in the adult sample), and equality of handedness across ADHD and control groups (not a matching variable). Any or all of these variables can be considered confounds in inferring developmental differences from our results. However, in regard to a history of stimulant usage as a confound, at least three fmri studies using medication-naïve children with ADHD have shown activation differences in similar regions of interest as those reported in this paper (e.g., IFG; Rubia et al., 2005; Silk et al., 2005; Smith et al., 2006). It appears that adults with ADHD may recruit alternative brain regions to perform task-related inhibition. Namely, task-related activation in the anterior cingulate was higher for parents with ADHD than control parents. Coupled with the absence of between-groups behavioral deficits, it may be that the adults with ADHD are relying on alternative brain regions to successfully inhibit responses during the task. Recruitment of the anterior cingulate cortex may be an attempt to recruit attentional mechanisms to improve performance (Peterson et al., 1999), perhaps related to response conflict or error monitoring. Interestingly, the finding of increased anterior cingulate cortex activity during successful no-go trials is consistent with results reported by Pliszka et al. (2006) using a pediatric sample. However, Bush et al. (1999) report lower activation in anterior cingulate cortex for adults with ADHD when performing a Counting Stoop task. Our finding of increased activation in anterior cingulate cortex on a behavioral inhibition task should be treated with caution given inconsistency across paradigms. Another developmental difference was the laterality of observed activation differences across youths and adults. Neurobiological models and previous findings largely support a right-lateralized deficit among patients with ADHD (see review by Giedd, Blumenthal, Molloy, & Castellanos, 2001). In our youth sample, most of the differences in brain activation were right lateralized and for those that appeared on the left (e.g., MFG, caudate), there were corresponding differences on the right side. For the adults, however, deficits were observed predominantly on the left side (i.e., lower activation in caudate nucleus and IFG). These findings may suggest

9 ADHD frontostriatal dysfunction 907 involvement of bilateral fronto-striatal circuitry in response inhibition, rather than simple right lateralization of this function due to strategic differences (e.g., use of language to help refrain from making a false alarm) in performing the task. The primary limitation of this study is small sample size. First, because of the decision to use the welldefined and comprehensively assessed MTA sample, potential participants were limited to the MTA sample. The effective sample was further limited to those children who continued to have ADHD into adolescence and who also had a parent with ADHD. Further, the number of data collection sites was limited to three in order to reduce heterogeneity introduced by using multiple scanners. The resulting sample size was small but comparable to other ADHD functional imaging studies. Nevertheless, this small sample may have limited power to detect betweengroup behavioral and brain activation differences. Another limitation was the use of a cross-sectional design to examine possible developmental differences in contrast to using a longitudinal design which would allow within-subjects comparison of developmental changes over time. Also, as commented on above, the youth and parent groups were not matched and displayed several baseline differences on key measures such as medication use history and gender. Study #2: Medication effects Methods Subjects. Of the 20 ADHD dyads, 7 youths and 5 parents were excluded because they were unable to perform with greater than 70% accuracy on the behavioral paradigm and have less than 3 mm of head movement for at least three runs. The remaining 13 youths and 15 parents comprised the sample. See Table 1 for demographic and diagnostic characteristics of the parent and youth samples. Analyses. On- and off-medication contrasts were conducted with a random effects analysis. Placebo and medication data sets were matched on number of runs used in the analysis to prevent differences in statistical power between the two conditions. A whole brain analysis was conducted, but contrasts were examined only in hypothesized ROIs that have been associated with methylphenidate response (i.e., striatum, prefrontal and posterior parietal cortices, and the cerebellum). Results and discussion Table 4 presents the results of paired t-tests examining medication effects on go/no-go task performance. Medication decreased reaction time (RT) variability and improved stimulus discrimination (i.e., d-prime) in both youths and their parents. In addition, medication reduced the number of errors of omission made by parents with ADHD. Comparisons of brain activation during performance of no-go trials showed that ADHD youths, while on MPH compared to placebo, showed a consistent pattern of increased activity across multiple regions. These task-related increases in activity were in the left middle frontal gyrus (Brodmann s area 9), left inferior frontal gyrus (Brodmann s area 45), right inferior parietal lobule (Brodmann s area 40), anterior cingulate (Brodmann s area 32), right caudate nucleus and left cerebellum (Figure 3). No regions of interest showed increased activation during placebo days compared to MPH days in the ADHD youths. Correlations between changes in behavioral performance on- and off-medication and brain activation beta values on- and off-medication in activation-defined regions of interest, where medication effects were noted, were non-significant (all p values >.05). Effects of medication on brain activation in parents with ADHD were less widespread. Increased activation was observed for the MPH condition compared to placebo in left caudate nucleus. However, increased activation was observed for placebo vs. MPH in the right inferior parietal lobule (Brodmann s area 40) and left middle frontal gyrus (Brodmann s area 46). See Table 5 for identified regions, volumes, and Talaraich coordinates. There appears to be a benefit of methylphenidate for youths with ADHD as evidenced by better behavioral performance and increased brain activa- Table 4 Paired t-test results, means and standard deviations for the behavioral performance of the ADHD youths and parents on placebo compared to on methylphenidate Youths Placebo (n ¼ 13) MPH (n ¼ 13) Effect size (d) t-tests Placebo (n ¼ 15) MPH (n ¼ 15) Effect size (d) t-tests Errors of omission 6.18 (8.27) 3.35 (7.52) (11.13) 1.44 (2.01) * Errors of commission (14.64) (13.65) (9.27) 7.72 (6.64) Reaction time 434 (80) 407 (42) (57) 403 (51) Reaction time SD (69.2) (40.5) ** (44.9) 78.0 (19.0) * D-prime 2.82 (.92) 3.37 (.84) * 3.12 (.78) 3.83 (.59) *** Note: *p <.05; **p < 01; ***p <.001.

10 908 Jeffery N. Epstein et al. Youths The right side of the image corresponds to the left side of the brain. Figure 3 Stimulant medication-related activation differences (on- vs. off-meds) during response inhibition for parent and youth participants. For parents, being on medication was related to significantly greater activation in left caudate nucleus. For youths, medication was related bilateral caudate and inferior parietal lobe activity a tion in fronto-striatal structures when on stimulant medication compared to placebo. This increased inactivation was observed in adults with ADHD for the striatum, but not the prefrontal cortex. In addition, adults with ADHD showed a medication-related decrease in activation of the right parietal lobe. The activation of striatal regions as a result of stimulant medication across youth and adults is consistent with the pharmacological properties of stimulants (Volkow, Wang, Fowler, & Ding, 2005), the existing literature (Vaidya et al., 1998), and our study hypotheses. MPH blocks dopamine reuptake in striatal areas and possibly blocks reuptake of norepinephrine in the cortices, thereby leading to medication-related increases in striatal activation. The increased activation in both prefrontal cortex and striatum observed in this youth sample is similar to the increase in activation observed in a sample of children with ADHD using fmri and a similar task (Vaidya et al., 1998). In addition to the medication effects reported in frontostriatal structures, the present study also reports medication-related increases in cerebellar activation among the youths. A similar effect was seen in the adults but this effect did not pass our statistical contiguity threshold, suggesting that the cerebellar effect is less pronounced and less widespread for adults. That stimulant medications would increase activation in the cerebellum is not surprising. The cerebellum has many inputs from the ventral tegmental area (Ikai, Takada, & Mizuno, 1994; Ikai, Takada, Shinonaga, & Mizuno, 1992), Table 5 Regions of interest showing significant differences in brain activation between medication and placebo conditions among youths and parents with ADHD Region of interest Brodmann s Area Talairach coordinates x y z Contrast Max. t-value Volume (mm 3 ) Youth Anterior cingulate MPH>Placebo Orbital frontal cortex (R)* MPH>Placebo Inferior frontal gyrus (L) 45 ) MPH>Placebo Caudate nucleus (R) MPH>Placebo Middle frontal gyrus (L) 9 ) MPH>Placebo Globus pallidus (R)* MPH>Placebo Caudate nucleus (L)* ) MPH>Placebo Inferior parietal lobule (R) )45 46 MPH>Placebo Cerebellum (L) )12 )62 )21 MPH>Placebo Middle frontal gyrus (L)* 46 ) Placebo>MPH Caudate nucleus (L) ) MPH>Placebo Hippocampus (L) )24 )18 )9 MPH>Placebo Inferior parietal lobule (R) 40 )37 )28 37 Placebo>MPH ) Cerebellum (L)* )2 )60 )17 MPH>Placebo *did not meet statistical contiguity threshold.

11 ADHD frontostriatal dysfunction 909 Table 6 Regions of interest showing significant differences in brain activation in control participants Table 7 Regions of interest showing significant differences in brain activation in participants with ADHD Region of interest Brodmann s area Talairach coordinates x y z Maximum t-value Region of interest Brodmann s area Talairach coordinates x y z Maximum t-value Youth Inferior frontal gyrus R R L 45 ) Middle frontal gyrus R L 9 ) Caudate nucleus (L) ) Globus Pallidus R L )18 ) Inferior parietal lobule (R) R ) L 40 )46 ) Cerebellum (L) )27 )65 ) Anterior cingulate R L 32 ) Orbital (R) Hippocampus (L) Inferior frontal gyrus R R L 46 ) Middle frontal gyrus R L 9 ) Caudate nucleus (R) Globus Pallidus R L ) Inferior parietal lobule (R) R ) L 40 )40 ) Cerebellum (L) )27 )64 ) Anterior cingulate R L 32 ) Orbital (L) ) Hippocampus (L) 31 )22 ) Youth Inferior frontal gyrus R R L 45 ) Middle frontal gyrus R L 9 ) Caudate nucleus (R) Globus Pallidus R L ) Inferior parietal lobule (R) R ) L 40 )45 ) Cerebellum (L) )22 )52 ) Anterior cingulate R L 32 ) Orbital (R) ) Hippocampus (L) 29 )29 ) Inferior frontal gyrus R R L 45 ) Middle frontal gyrus R L 46 ) Caudate nucleus (R) Globus Pallidus R L ) Inferior parietal lobule (R) R ) L 40 )53 ) Cerebellum (L) )36 )55 ) Anterior cingulate R L 32 ) Orbital (R) Hippocampus (L) ) which is a dopaminergic center of the brain. Using PET, increased cerebellar activity as a result of MPH has been shown with normal adults (Volkow et al., 1998). This study s documentation of increases in cerebellar activation as a result of MPH administration does differ from previous studies in that this effect was shown using task-related BOLD response. Typically, fmri studies have not included cerebellar regions in their imaging field of view. Hence, the present findings emphasize the importance of collecting whole brain images when examining activation patterns, and especially medication effects, in patients with ADHD. A limitation of the current study is that children and adults with ADHD did not necessarily take an individually-determined optimal dose of MPH on the day of scanning. Rather, a mg/kg formula (with a maximum cut-off of 20 mg MPH) was used to assign dosages to individual participants. Because weight does not correlate with optimal dose (Rapport, Du- Paul, & Kelly, 1989) and because patients have variable dose response curves (Rapport et al., 1987), dosing according to weight does not always result in an optimal dosage for each individual. General discussion Results from the above studies support prevailing theoretical notions that ADHD is caused by a neurobiological deficit localized to prefrontal and striatal regions of the brain. Several studies have been

12 910 Jeffery N. Epstein et al. conducted examining response inhibition-related brain activation in children with ADHD. This study extends findings of fronto-striatal dysfunction to adults with ADHD and in individuals with genetic vulnerability for the disorder. A stimulant medication trial among the ADHD youths and their parents showed that among youths, stimulant medication increases activation in the striatum, as well as in cerebellar regions. Increases in activation were observed in prefrontal regions in the youth, but not the adults. In addition, there was a medicationrelated increase in activation in hippocampus for the adults, unlike the youths. A clear strength of this study was the use of the MTA sample for recruitment of participants. All of the MTA participants with ADHD had a documented history of an ADHD, Combined Type diagnosis between 7 and 9 years of age. Further, children in this sample were assessed approximately every 2 years until the present so that persistence of ADHD symptomatology has been documented. This differs from most ADHD imaging samples where study inclusion is usually, but not always, based upon symptoms present at the time of the study. Such a single time point diagnosis may yield increased sample heterogeneity, with the likelihood of multiple etiologies (Biederman, 2005). Our study sample was further restricted to youths and parents with a family history of ADHD. This recruitment strategy increases the likelihood that the observed findings represent the effects of stimulant medications on youths and adults whose ADHD symptomatology is biologically based and shared within dyads. Because the sample used for the present study was selected based on genetic association, the study results provide several clues as to possible endophenotypic characteristics that may be used to study the genetics and pharmacogenetics of ADHD. Indeed, several investigators (e.g., Doyle et al., 2005) have described the advantages of using endophenotypes to search for genetic associations with ADHD. In addition, several research studies report on the relationships between specific candidate genes and response to stimulant medication among patients with ADHD (see McGough, 2005 for review). Some of the outcomes in the present study (i.e., d-prime on a response inhibition task, fronto-striatal activation) demonstrated between-group and medicationrelated effects across developmentally different samples. Relating these endophenotypes to genetic markers has the potential to identify genetic predictors of ADHD and possibly stimulant response. Acknowledgements The grant was supported by a set of NIH collaborative grants: MH (Epstein), MH (Casey), MH (Reiss), MH (Hinshaw), and MH (Greenhill) as well as grant #RR09784 (Glover). Correspondence to Jeffery N. 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