hyperactivity disorder

Psychiatry Research: Neuroimaging 202 (2012) 245–251

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White matter abnormalities associated with disruptive behavior disorder in adolescents with and without attention-deficit/hyperactivity disorder Yang Wang a,⁎, Kelly K. Horst a, William G. Kronenberger b, Tom A. Hummer b, Kristine M. Mosier a, Andrew J. Kalnin c, David W. Dunn b, Vincent P. Mathews a a b c

Department of Radiology and Imaging Sciences, IU Center for Neuroimaging, Indianapolis, IN, USA Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA Department of Radiology, Ohio State University College of Medicine, Columbus, OH, USA

a r t i c l e

i n f o

Article history: Received 7 September 2011 Received in revised form 9 January 2012 Accepted 11 January 2012 Keywords: Disruptive behavior disorder Attention-deficit/hyperactivity disorder Adolescent Diffusion tensor imaging White matter

a b s t r a c t Disruptive behavior disorders (DBD) are among the most commonly diagnosed mental disorders in children and adolescents. Some important characteristics of DBD vary based on the presence or absence of comorbid attention-deficit/hyperactivity disorder (ADHD), which may affect the understanding of and treatment decision-making related to the disorders. Thus, identifying neurobiological characteristics of DBD with comorbid ADHD (DBD + ADHD) can provide a basis to establish a better understanding of the condition. This study aimed to assess abnormal white matter microstructural alterations in DBD + ADHD as compared to DBD alone and healthy controls using diffusion tensor imaging (DTI). Thirty-three DBD (19 with comorbid ADHD) and 46 age-matched healthy adolescents were studied using DTI. Fractional anisotropy (FA), and mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) were analyzed using tract-based spatial statistics (TBSS). Significantly lower FA and higher MD, RD and AD in many white matter fibers were found in adolescents with DBD + ADHD compared to controls. Moreover, lower FA and higher RD were also found in the DBD + ADHD versus the DBD alone group. Alterations of white matter integrity found in DBD patients were primarily associated with ADHD, suggesting that ADHD comorbidity in DBD is reflected in greater abnormality of microstructural connections. © 2012 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Oppositional-defiant disorder (ODD) and conduct disorder (CD), collectively referred to as disruptive behavior disorders (DBD), involve persistent symptoms of defiant, disobedient, aggressive and hostile behavior, particularly towards authority figures. This consistent behavior pattern results in problems such as arguing, rule-breaking and, in more extreme forms, aggressive criminal acts (American Psychiatric Association, 1994; Kronenberger and Meyer, 2001; Loeber et al., 2009). The DBD diagnoses are among the most common childhood mental disorders, with CD occurring in 1% to 4% of children and adolescents aged 9 to 17 years and ODD occurring in 1% to 6% of the population (Findling, 2008). The difficulty of treating DBD can be compounded by its high comorbidity with attention-deficit/hyperactivity disorder (ADHD) (Loeber et al., 2000; Burke et al., 2002; Ollendick et al., 2008), as well as depression, substance use, and other conditions (Burke et al., 2002). Thus, identifying neurobiological characteristics of DBD with comorbid disorders can help to improve understanding of these ⁎ Corresponding author at: Department of Radiology and Imaging Sciences, Indiana University School of Medicine; 950 West Walnut St., R2 E124, Indianapolis, IN 46202, USA. E-mail address: [email protected] (Y. Wang). 0925-4927/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pscychresns.2012.01.005

conditions and ultimately contribute to the development of more effective treatments. Depending on the presence or absence of comorbid ADHD, children and adolescents with DBD may differ in behavioral and neuropsychological characteristics, particularly executive functioning (Oosterlaan et al., 2005; Hummer et al., 2011). Children with ADHD demonstrate inattention, disorganization, impulsivity and hyperactivity, which disrupt the child's functional and adaptive behaviors (American Psychiatric Association, 1994; Kronenberger and Meyer, 2001; Barkley, 2005). These symptoms can be particularly detrimental when DBD is diagnosed with comorbid ADHD, as impulsivity and poor self-regulation may amplify the defiant behavior that characterizes DBD. Much of the published evidence supporting neurobiological models of DBD comes from behavioral performance measures rather than direct assessments of brain functioning (Loeber et al., 2000; Burke et al., 2002; Loeber et al., 2009). As a result, studies identifying the neural deficits that are uniquely related to the development of DBD in youth are still needed (Loeber et al., 2009). Furthermore, there have been very few studies of the role of comorbid ADHD in the neurobiology of DBD. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that provides in vivo information about the direction and integrity of neural fiber tracts (Alexander et al., 2007). Because DTI

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is still an emerging technique, only a limited number of DTI studies to date have investigated white matter differences associated with psychiatric disorders of childhood and adolescence. However, there is converging evidence suggesting that white matter abnormalities are associated with ADHD (Ashtari et al., 2005; Casey et al., 2007; Makris et al., 2008; Pavuluri et al., 2009; Silk et al., 2009). These previous DTI studies mainly focused on fractional anisotropy (FA) as a measure of brain tissue integrity, since FA measures the degree to which water molecules diffuse in a given direction, reflecting white matter fiber density (Beaulieu, 2002). Alternatively, overall diffusivity in a tissue can be quantified by mean diffusivity (MD), which is a directionally averaged measure of the apparent diffusion coefficient and may help to better understand white matter structure (Alexander et al., 2007). Some studies also investigated eigenvalues of the diffusion tensor or their related quantities of radial diffusivity (RD) and axial diffusivity (AD) (Schmithorst and Yuan, 2010). RD and AD can provide more specific information about directional changes in white matter integrity (Alexander et al., 2007). In recent DTI studies, these four DTI parameters, FA, MD, RD and AD, were very useful in characterizing normal regional development in white matter microstructure throughout adolescence (Ashtari et al., 2007; Qiu et al., 2008; Asato et al., 2010; Bava et al., 2010; Schmithorst and Yuan, 2010). Thus far, there are only a very limited number of DTI research studies in DBD samples. Our previously conducted DTI study had assessed structural abnormalities in subjects with DBD compared to healthy controls (Li et al., 2005), where we found significant differences in regions including the anterior region of the corona radiata and bilateral superior longitudinal fasciculus, potentially indicating deficits in connection between frontal and subcortical or parietal regions in DBD. However, it failed to investigate the potential confound of comorbid ADHD among youth with DBD, a limitation that this current study aims to address. The present study is further delineated from prior research by using an automated, unbiased whole-brain analysis method of tract-based spatial statistics (TBSS) (Smith et al., 2006). We examined the microstructural properties of white matter in adolescents with diagnoses of DBD and comorbid ADHD (DBD + ADHD), DBD alone (DBD − ADHD), and controls with no psychiatric diagnosis. To the best of our knowledge, our study is the first to assess DTI characteristics in adolescents with DBD with and without ADHD. As a result, this study offers the potential to contribute important novel information about DBD diagnoses and comorbidities. 2. Methods 2.1. Participants The study was approved by the local Institutional Review Board, and written informed consent was obtained from subjects and their caregivers prior to any study procedures. Adolescents (13–17 years) with and without DBD diagnoses were recruited via informational flyers posted in community settings. Diagnoses of ODD, CD, and/or ADHD were made based on results of the Schedule for Affective Disorders and Schizophrenia for School-Aged Children, Present and Lifetime Version (K-SADS) (Kaufman et al., 1996). The semistructured Diagnostic Interview (Behavior Disorders Module) was performed. Thirty-three subjects with a DBD diagnosis (ODD: n = 11; CD: n = 22) met criteria for the present study. These criteria included diagnosis of ODD or CD based on the K-SADS, completion of a valid MRI DTI scan, and presence of at least one recurrent Conduct Disorder symptom of aggressive behavior toward people or animals within the past 6 months (as determined with the K-SADS interview). The presence of an aggressive symptom was required to differentiate between subjects with significant aggressive/antisocial DBD behaviors as compared to more minor, avoidant, rule-breaking behaviors. In additional, 46 age matched healthy controls were recruited using the same method (average age; control: 15.4± 1.2 years; DBD: 15.3± 1.5 years). Participants in the DBD alone group (DBD − ADHD; n = 14) were required to have three or fewer inattentive ADHD symptoms and to have three or fewer hyperactiveimpulsive ADHD symptoms, based on the K-SADS semistructured diagnostic interview. Participants in the DBD with comorbid ADHD group (DBD + ADHD; n = 19) met criteria for ADHD (any subtype), based on the K-SADS interview. Participants with no psychiatric disorders (healthy controls [HC]; n = 46) had no DSM-IV diagnosis, based on results of the K-SADS interview and the Adolescent Symptom Inventory-4 (ASI-4) (Gadow and Sprafkin, 1998) parent-report behavior checklist, and had three or fewer inattentive ADHD symptoms and three or fewer hyperactive-impulsive ADHD

symptoms on the K-SADS. Adolescents in the HC group also had no contact with a mental health professional for treatment of a behavioral or emotional problem within the past 3 years. Participants with a current diagnosis of major depressive disorder or substance abuse/dependence were excluded from the study, as were those with a current or past diagnosis of bipolar disorder or schizophrenia. No subject (of both control and DBD groups) had a history of brain injury. Seven participants with DBD were taking psychotropic medication (three mixedsalts amphetamines (Adderall); one bupropion; one methylphenidate and atomoxetine; one bupropion and methylphenidate; and one methylphendiate, aripiprazole, oxcarbazepine and citalopram) for their disorder. In order to limit potential effects on DTI measurements, those subjects were requested to withhold their stimulant medications for at least 24 hours (> 3 half-lives) prior to the start of the study session during which MRI was conducted. All medication instructions were performed under the direction of the study psychiatrist (D.W.D.). 2.2. Procedure Individuals participated in two separate study visits. During the first visit, subjects completed a psychological evaluation consisting of clinical interviews, questionnaires and neurocognitive tests of executive functioning. Adults identifying themselves as the primary caregiver for the teens completed questionnaires as well. Diagnostic measures completed at the first visit included the K-SADS (Kaufman et al., 1996) and ASI-4 (Gadow and Sprafkin, 1998). IQ was screened using the Matrices (nonverbal) subtest of the Kaufman Brief Intelligence Test (K-BIT) (Kaufman and Kaufman, 1990). To assess executive functioning behaviors in everyday life, caregivers filled out the Behavior Rating Inventory of Executive Function (BRIEF) (Gioia et al., 2000). The BRIEF yields a Behavioral Regulation Index (BRI) and a Metacognition Index (MI), reflecting impulsebehavioral and attentional-cognitive (respectively) aspects of executive functioning in daily behavior. The second visit involved MRI scanning. The MRI measurements were acquired on a 3 T Tim Trio scanner (Siemens, Germany) using an eight-channel phased array head coil. DTI was measured along 60 non-collinear directions. A single-shot spin-echo echoplanar DTI sequence was performed using the following parameters: matrix = 128 × 128; field of view= 256 × 256 mm; echo time/repetition time = 100/10100ms; 60 transversal continuous slices with 2-mm thickness; diffusion-weighted factor b = 1000 s/mm2; additional 10 images without use of a diffusion gradient (b = 0 s/mm2). 2.3. Data analysis Head movement during DTI acquisition was measured using the AFNI software (http://afni.nimh.nih.gov/afni/) by registering all DTI images to the first b0 image using a 12 degree-of-freedom affine correction with mutual information as the cost function (Ling et al., 2012). No significant difference was found between groups in maximal rotation (in degree; HC: 0.44 ± 0.30; DBD − ADHD: 0.43 ± 0.32; DBD + ADHD: 0.54 ± 0.23; F = 0.862, p = 0.43) or maximal displacement (in mm; HC: 0.84± 0.46; DBD − ADHD: 0.74 ± 0.47; DBD + ADHD: 0.83± 0.44; F = 0.282, p = 0.76). The DTI data were further analyzed using the FSL package (FMRIB Center, Oxford, United Kingdom). Preprocessing included correction for motion and eddy current effects in DTI images. FMRIB's Diffusion Toolbox (Behrens et al., 2003) was used to fit the tensor model and to compute the FA, MD, RD and AD maps. Next, voxel-wise TBSS analysis was performed with the following steps (Smith et al., 2006). All individual FA maps were nonlinearly registered to the template and then affine-transformed into standard Montreal Neurological Institute (MNI) space. A mean skeleton map of white matter tracts was generated based on the mean FA image of all subjects. Each subject's aligned FA image was projected onto the FA skeleton, resulting in a skeletonized FA map for each individual. TBSS analyses of MD, RD and AD were conducted in the same manner and aligned to the FA skeleton. Finally, all skeletonized DTI maps were fed into a voxel-wise group ANCOVA (analysis of covariance) using a General Linear Model approach with age and gender as covariates. Inference on these statistics was carried out using the “randomise” program within FSL, which performs permutation testing that does not rely on a Gaussian distribution (Westfall and Young, 1993; Nichols and Holmes, 2002). We used threshold-free cluster enhancement (TFCE), a new method for finding significant clusters in MRI data without having to define them as binary units (Smith and Nichols, 2009). The statistics were built up over 5000 random permutations with the maximum TFCE recorded at each permutation. The 95th percentile of this distribution was then used as a TFCE threshold and the significance level calculated from this distribution. Thus, significant clusters were fully corrected for familywise error at p b 0.05 (Westfall and Young, 1993; Smith and Nichols, 2009). With this approach, voxel-wise post-hoc comparisons between groups were assessed. Age and gender were also included as covariates. Anatomical localization of each significant cluster was determined using the pertinent available anatomic templates (ICBM-DTI-81 parcellation map and Johns Hopkins University DTI-based WM atlas) (Mori et al., 2008). In an additional exploratory analysis, the presence of correlations between BRIEF measures of executive functioning (BRI and MI scores) and mean DTI indices was assessed. Regions of interest (ROIs) were created from clusters showing significant differences of FA in the contrast of DBD + ADHD versus healthy subjects. An individual mean DTI index value of each ROI was extracted per subject, which was implemented using the FSL package. Partial correlation analysis controlling for age and gender was conducted using SPSS 17.0 (SPSS Inc., Chicago, IL).

Y. Wang et al. / Psychiatry Research: Neuroimaging 202 (2012) 245–251 Table 1 Demographics.

Age Gender (male:female) Education (years) K-BIT Matrices Standard Score (IQ) DBD diagnosis (ODD:CD) K-SADS ODD/CD symptoms K-SADS ADHD symptoms ASI-4 combined ADHD T-score BRIEF behavioral regulation BRIEF metacognition

DBD + ADHD (n = 19)

DBD − ADHD (n = 14)

HC (n = 46)

15.4 (1.2) 16:3 9.4 (1.5) 103 (15) 8:11 6.0 (1.7)a 11.4 (2.9)a b 76.8 (12.5) a b 77.2 (12.0) a b 72.3 (9.8) a b

15.1 (1.5) 8:6 9.1 (1.5) 102 (12) 3:11 5.2 (2.1)a 2.4 (3.1)c 61.4 (10.5) 65.6 (11.1) 59.4 (8.1)a

15.4 (1.5) 36:10 9.4 (1.2) 109 (11) 0 0 (0) 0.3 (1.3) 47.2 (7.2) 46.3 (7.0) 50.3 (8.6)

a a

Note: Demographic data include mean (standard deviation) for adolescents with DBD and comorbid ADHD (DBD + ADHD), DBD without ADHD (DBD − ADHD) and healthy controls (HC). Symbols: a group means significantly differ from HC group at p b 0.001; b group means significantly differ from the DBD − ADHD group at p b 0.001; c group means significantly differ from HC group at p b 0.01.

3. Results 3.1. Sample characteristics The three diagnostic groups (DBD + ADHD, DBD − ADHD and HC) were comparable on demographic characteristics, education years and IQ, and differed in expected ways on the diagnostic measures (Table 1). DBD + ADHD and DBD − ADHD groups did not differ in diagnosis of ODD and CD (Fisher's Exact Test, p = 0.278). Although no significant difference was found in gender between DBD + ADHD

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and DBD − ADHD groups (Fisher's Exact Test, p = 0.122), gender was not exactly balanced across groups (Table 1); thus this potential confound was addressed by using gender as a covariate in further statistical analyses. Both measures of executive function from the BRIEF questionnaire indicated that the DBD + ADHD sample demonstrated more executive functioning deficits than both the DBD − ADHD and control groups in daily behavior. 3.2. Voxel-wise analysis Across the DTI measures (Table 2; explained in detail below), widespread white matter tracts showed significant difference between the DBD + ADHD and the HC groups, while several clusters were found showing significant differences between the DBD + ADHD and DBD − ADHD groups (Figs. 1 and 2). Significantly lower FA in the DBD + ADHD sample compared to the HC sample, indicating alterations in microstructural tissue integrity, was found in the corpus callosum. Multiple projection fibers, including bilateral anterior limb of internal capsule, right posterior limb of internal capsule, and bilateral anterior, superior and posterior corona radiata demonstrated significantly lower FA in DBD + ADHD patients relative to the HC sample, as did association fibers in bilateral superior longitudinal fasciculus, bilateral inferior fronto-occipital fasciculus, bilateral posterior thalamic radiation, bilateral sagittal stratum, right external capsule, left cingulum and right fornix. No region was found to have significant elevation of FA in the DBD + ADHD group as compared to the HC group. Likewise, the DBD + ADHD group showed significantly higher MD in more extensive clusters, in addition to the significant areas found on FA, located in the corpus callosum, almost

Table 2 Clusters location and size (mm3) showing significant differences between groups. Tract type

Commissural

Projection

Association

Tract

Genu of corpus callosum Body of corpus callosum Splenium of corpus callosum Left anterior limb of internal capsule Right anterior limb of internal capsule Left posterior limb of internal capsule Right posterior limb of internal capsule Left anterior corona radiata Right anterior corona radiata Left superior corona radiata Right superior corona radiata Left posterior corona radiata Right posterior corona radiata Left superior longitudinal fasciculus Right superior longitudinal fasciculus Left superior fronto-occipital fasciculus Right superior fronto-occipital fasciculus Left uncinate fasciculus Right uncinate fasciculus Left inferior fronto-occipital fasciculus Right Inferior fronto-occipital fasciculus Left posterior thalamic radiation1 Right posterior thalamic radiation1 Left sagittal stratum2 Right sagittal stratum2 Left external capsule Right external capsule Left cingulum (cingulate gyrus) Right cingulum (cingulate gyrus) Left fornix (crus)/stria terminalis Right fornix (crus)/stria terminalis

DBD − ADHD vs. DBD ± ADHD

HC vs. DBD ± ADHD FA⁎

MD⁎⁎

RD⁎⁎

AD⁎⁎

FA⁎⁎⁎

RD⁎⁎⁎⁎

1109 1362 1223 338 143

1143 2165 1697 412 368 356 343 1333 1303 1096 1188 571 581 1107 1121 58 66 35 39 341 199 703 786 386 536 330 459 323 284 116 226

1194 1967 1603 419 365 131 212 1238 1261 946 1157 496 530 931 1081 59 57 33 37 362 265 641 819 355 494 267 304 314 228 115 191

850 1582 1497 270 248 505 324 1212 909 961 681 502 428 644 341 45 58 29 40 143 115 568 444 369 433 383 380

739 1438 385

500 1348 311

773 1154 417 167 285

884 1013 447 400 377 81

99 619 690 54 649 41 216 318 612 50

286 226 266 690 24 315 126 91

100

171 42 205

128 100 210

Cluster locations and size (mm3) of significant differences between healthy controls (HC) and DBD with comorbid ADHD (DBD+ ADHD), and between DBD without ADHD (DBD− ADHD) and DBD + ADHD in fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Data indicate significant cluster size at p b 0.05 (corrected for multiple comparisons). Symbols:*HC > DBD + ADHD; **DBD + ADHD > HC; ***DBD − ADHD > DBD + ADHD; ****DBD + ADHD > DBD − ADHD. No differences were present in opposite directions. 1Posterior thalamic radiation includes optic radiation; 2Sagittal stratum includes inferior longitudinal fasciculus.

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Fig. 1. Regions of significantly lower fractional anisotropy (FA), higher mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) in DBD comorbid with ADHD (DBD+ ADHD) compared with healthy control (HC) subjects (p b 0.05, corrected for multiple comparisons). Green color indicates white matter skeleton where no significant results were found; the red color indicates significantly lower FA in DBD + ADHD compared to the HC group; and the blue color designates significantly higher MD, RD or AD in DBD + ADHD group. Statistical clusters were dilated from the white matter skeleton for visualization purposes. (Note: L = left side of the brain; R = right side of the brain; SCR = Superior corona radiata; SLF = Superior longitudinal fasciculus; ACR= Anterior corona radiata; PCR= Posterior corona radiata; BCC = Body of corpus callosum; GCC = Genu of corpus callosum; SCC = Splenium of corpus callosum; PTR(OR) = Posterior thalamic radiation (including optic radiation); ALIC= Anterior limb of internal capsule; EC = External capsule; SS(ILF) = Sagittal stratum (including inferior longitudinal fasciculus; IFOF= Inferior fronto-occipital fasciculus).

Fig. 2. Regions of significantly lower fractional anisotropy (FA) and higher radial diffusivity (RD) in DBD comorbid with ADHD (DBD+ ADHD) compared with DBD alone (DBD− ADHD) group (p b 0.05, corrected for multiple comparisons). Green color indicates white matter skeleton where no significant results were found; the red color shows significantly lower FA in DBD + ADHD compared to DBD − ADHD group; and the blue color illustrates significantly higher RD in DBD − ADHD group. Statistical clusters were dilated from the white matter skeleton for visualization purposes. (Note: L = left side of the brain; R = right side of the brain; ACR = Anterior corona radiata; SCR = Superior corona radiata; PCR = Posterior corona radiata; BCC = Body of corpus callosum; GCC = Genu of corpus callosum; SCC = Splenium of corpus callosum; IFOF= Inferior fronto-occipital fasciculus).

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all major projection fibers and all major association tracts. Moreover, areas with higher MD in the DBD + ADHD group primarily demonstrated significantly higher RD and AD. No region was found to have significantly lower diffusivity indices in DBD + ADHD group. In comparison with the DBD − ADHD group, the DBD + ADHD group showed significantly lower FA mainly in the corpus callosum, as well as some projection fibers in bilateral anterior, bilateral superior and right posterior corona radiata (Fig. 2). No region showed lower FA in the DBD − ADHD group relative to the DBD + ADHD group. Furthermore, areas with significantly lower FA in the DBD + ADHD group relative to the DBD − ADHD group also tended to have significantly greater RD in the DBD + ADHD group (Fig. 2). Differences between DBD− ADHD and HC did not reach the stringent statistical threshold of this study (TFCE corrected p b 0.05), although trends of difference between DBD − ADHD and healthy controls were found at very liberal threshold (p b 0.10), in addition, difference between all DBD patients (including DBD+ ADHD and DBD −ADHD) and control subjects was found at a liberal threshold (pb 0.05, uncorrected), mainly in corona radiata and longitudinal fasciculus. 3.3. ROI analysis Using clusters showing significant FA differences between the DBD + ADHD and HC groups as ROIs, partial correlation analysis controlled for sex and age revealed a significant negative correlation between BRIEF scores and FA of the corpus callosum splenium (BRI: r = −0.341, p = 0.002; MI: r = −0.370, p = 0.001), corpus callosum genu (BRI: r = − 0.249, p = 0.029; MI: r = −0.220, p = 0.055), bilateral posterior thalamic radiation (BRI: r = −0.333, p = 0.003 for left side and r = − 0.251, p = 0.028 for right side; MI: r = −0.305, p = 0.007 for left side and r = − 0.289, p = 0.011 for right side), left superior longitudinal fasciculus (BRI: r = −0.257, p = 0.024; MI: r = −0.326, p = 0.004), and right superior longitudinal fasciculus (MI: r = −0.231, p = 0.043) (Fig. 3). 4. Discussion In this DTI study, white matter abnormalities were most evident in adolescents with DBD + ADHD, including most major commissural, projection and association fibers. These findings are consistent with brain regions implicated in prior studies of adolescents with DBD, although little prior research has investigated the role of ADHD comorbidity (Kronenberger et al., 2005; Li et al., 2005; Mathews et al., 2005; Sterzer et al., 2005, 2007; Herpertz et al., 2008; Huebner et al., 2008; Decety et al., 2009). Differences in DTI measures across key white matter tracts were found between teens with DBD + ADHD and healthy

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controls, also between teens with DBD + ADHD compared to those with DBD − ADHD, suggesting that ADHD comorbidity in DBD is reflected in greater white matter abnormality. These structural differences align with neuropsychological reports indicating that teens with DBD + ADHD showed worse performance of tasks assessing executive function than DBD − ADHD and control groups (Oosterlaan et al., 2005; Hummer et al., 2011). Due to methodological discrepancy, the results of developmental DTI studies vary as to the regional specificity of age-related changes (Ashtari et al., 2007; Qiu et al., 2008; Asato et al., 2010; Bava et al., 2010; Schmithorst and Yuan, 2010). In general, though, a person's age is positively associated with FA, and inversely related to diffusivity indices, particularly RD (Ashtari et al., 2007; Qiu et al., 2008; Asato et al., 2010; Bava et al., 2010; Schmithorst and Yuan, 2010). While FA and MD are commonly used DTI measures that reflect the maturity of neuronal connections, RD and AD may be more closely connected to changes in tissue morphology related to axonal myelination and/or organization during adolescence (Beaulieu, 2002; Ashtari et al., 2007). Increased RD in adolescents with DBD + ADHD of this study could be associated with delayed or disrupted development of the myelin sheath (Song et al., 2002). On the other hand, increased AD in youth with DBD + ADHD may be related to the poor growth of neurofibrils, such as microtubules and neurofilaments, and the abnormal development of glial cells (Qiu et al., 2008). Based on our findings, therefore, one could speculate that abnormal or delayed development of white matter in some brain regions has occurred in adolescents with DBD and especially DBD + ADHD. While DTI research in DBD patients still remains sparse, abnormalities in many white matter regions reported in the present study have been previously identified in DTI studies using ADHD samples, and therefore might reflect neurobiological correlates of these symptoms specifically (Bush, 2010). For instance, a majority of DTI studies in ADHD has demonstrated decreased FA in the superior longitudinal fasciculus (Hamilton et al., 2008; Makris et al., 2008; Pavuluri et al., 2009; Silk et al., 2009; Konrad et al., 2010). The superior longitudinal fasciculus is a long associational fiber connecting prefrontal to parietal cortex, meaning it likely plays a prominent role in higher-level cognitive processes, including attention and executive functions. Abnormal findings of lower FA in the superior longitudinal fasciculus and corticospinal tract have suggested disruption of attention and motor networks in ADHD (Hamilton et al., 2008), while Silk et al. indicated fronto-striatal and fronto-parietal circuitry abnormalities in children with ADHD (Silk et al., 2009). Recently, decreased FA and abnormalities across various white matter tracts were reported in pediatric samples of ADHD (Pavuluri et al., 2009). Together, our DTI data present some degree of consistency with white matter abnormalities

Fig. 3. 3D scatterplots showing fractional anisotropy (FA) of regions-of-interest (corpus callosum, posterior thalamic radiation and superior longitudinal fasciculus) in relationship with BRIEF BRI and MI T scores. (HC = health control; DBD − ADHD = DBD only; DBD + ADHD = DBD comorbid with ADHD).

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associated with ADHD, although none of the previous DTI studies investigated changes in comorbid DBD and ADHD. Differences in long-range white matter tracts in frontal, parietal and temporal areas found in this study may correspond to differential activation or morphometry in imaging studies among individuals with DBD (Mathews et al., 2005; Sterzer et al., 2005, 2007; Herpertz et al., 2008; Huebner et al., 2008; Decety et al., 2009). Subjects with DBD have demonstrated abnormal activation or volume of frontal and temporal regions associated with executive function and cognitive/emotional self-control (Mathews et al., 2005; Wang et al., 2009). However, the intervening role of comorbid ADHD has been difficult to discern, particularly since the majority of such patients in these studies have both disorders. In a few neuroimaging investigations on non-comorbid DBD, Rubia and colleagues have demonstrated low task-dependent activation in boys with conduct disorder, associated with deficits in motivation, emotion, and performance monitoring (Rubia et al., 2008, 2010). This activity differs somewhat from boys with non-comorbid ADHD alone, who demonstrate hypoactivation more related to cognitive control and inhibitory deficits (Rubia et al., 2008, 2010). These findings indicate that while both disorders may be associated with neural abnormalities, the specific symptoms of each disorder are indeed differentially reflected neurobiologically. Thus, although overlaps certainly exist, “emotional” regions and networks may be affected in youth with DBD, whereas poor development of networks involved in higher-level cognitive processes is likely related to ADHD symptoms (Rubia, 2011). These neurobiological and neuropsychological deficits are combined in youth with comorbid DBD + ADHD. The current report supports the notion that DBD may be characterized by fronto-parietal and frontostriatal system deficiencies that mediate executive functioning and emotional control, particularly when ADHD is also present. Our exploratory analysis revealed that observer-reported behaviors of executive dysfunction, a key marker of ADHD, were significantly correlated with FA measures in the corpus callosum, bilateral superior longitudinal fasciculus and posterior thalamic radiation. The corpus callosum connects the two cerebral hemispheres, and callosal abnormalities might therefore affect interhemispheric communication in ADHD (Silk et al., 2009; Bush, 2010; Konrad et al., 2010). There is evidence indicating regional specificity, with anterior corpus callosum (genu) abnormalities suggesting abnormal prefrontal connections, as well as posterior corpus callosum (splenium) abnormalities signifying parietal and temporal lobe connection problems (Pavuluri et al., 2009; Bush, 2010). This result coincides with a multimodal MRI study showing a correlation of FA in prefrontal fiber tracts and a measure of impulsivity in ADHD (Casey et al., 2007). Similarly, an additional report found a correlation between measures of attention and DTI parameters in the right superior longitudinal fasciculus (Konrad et al., 2010). Thus, improper development of the superior longitudinal fasciculus may be associated with ADHD symptoms, and may have a pronounced behavioral effect in children with comorbid DBD and ADHD. Moreover, we also found significant difference in DTI measures between DBD + ADHD and DBD − ADHD groups. Assuming that the study findings do reflect a difference in the neurobiological underpinnings of DBD, the results imply a more significant role for neuroanatomic, physiologic, or genetic factors (affecting white matter integrity) in DBD + ADHD, as compared to DBD without accompanying ADHD. These potential differences in etiology may also suggest how treatment may differ based on ADHD comorbidity. When DBD is present alone without comorbid ADHD, the cause of the disorder may be more likely related to non-biological factors, such as environmental, social, and/or behavioral influences (Dodge, 1993; Cadoret et al., 1995; Hummer et al., 2011). As a result, the treatment focus may be best placed on behavioral and social-cognitive factors. However, when ADHD is present as a comorbid disorder, more aggressive or biologically informed approaches should be integrated into the treatment, since the presence of ADHD

may be a contributing factor to the development of DBD (Dodge, 1993; Cadoret et al., 1995). Interestingly, trends toward difference between DBD − ADHD and healthy controls were found only at a very liberal statistical threshold. The significance of this difference may have been affected by the limited power of this study due to the relatively small sample size (DBD − ADHD: n = 14). Likewise, we have observed a difference between the whole DBD sample and controls at a liberal threshold, which is in accord with the previous DTI study (Li et al., 2005). However, that report did not investigate the impact of comorbid ADHD on the finding. Therefore, differences found previously might have been attributed in part to ADHD comorbidity. It is important to note that we did not include an ADHD-only (i.e., no DBD diagnosis) subsample. Hence, our results provide a characterization of altered white matter connections and a demonstration of abnormal microstructural integrity in DBD and DBD with ADHD comorbidity, but should not be simply applied to the population of individuals with ADHD alone (no comorbidity). More advanced investigation will be needed to determine whether these observed abnormalities are due to primary problems with the white matter tracts themselves, are secondary to pathology in the regions that the white matter tracts connect, or reflect some combination of effects (Bush, 2010). In addition, some reports suggested that DTI measures could be affected by motion or physiological noise (Walker et al., 2011; Ling et al., 2012), which could be problematic among youth with DBD + ADHD, although we found no difference in rotation and displacement caused by head motion during DTI between groups. There is no well-accepted method to eliminate such artifacts and future research is warranted (Ling et al., 2012; Walker et al., 2011). In addition, future investigation should also control for potential confounds including demographics or other unforeseen group differences (sport activities, etc.). In conclusion, this investigation indicates that alterations of white matter integrity found in a group of adolescents with DBD were primarily associated with ADHD, suggesting that ADHD comorbidity in DBD is reflected in greater abnormality of microstructural connections. DTI changes along key white matter pathways in DBD with ADHD patients are associated with measures of executive function. The study of white matter characteristics associated with DBD diagnoses is important for several reasons. First, enhanced understanding of the neurobiological characteristics of psychiatric diagnoses is an important step in developing theories about their etiology and course. Second, there is some evidence for subtypes of DBD based on ADHD comorbidity, and investigation of white matter characteristics may provide further evidence and understanding of that differentiation. Third, neurobiological characteristics and/or differences in DBD diagnoses and subtypes may provide information that can be used to help in understanding and identifying appropriate treatments for those disorders. Acknowledgments This study was supported by a grant from the Center for Successful Parenting, Indiana. References Alexander, A.L., Lee, J.E., Lazar, M., Field, A.S., 2007. Diffusion tensor imaging of the brain. Neurotherapeutics 4, 316–329. American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Author, Washington, DC. Asato, M.R., Terwilliger, R., Woo, J., Luna, B., 2010. White matter development in adolescence: a DTI study. Cerebral Cortex 20, 2122–2131. Ashtari, M., Kumra, S., Bhaskar, S.L., Clarke, T., Thaden, E., Cervellione, K.L., Rhinewine, J., Kane, J.M., Adesman, A., Milanaik, R., Maytal, J., Diamond, A., Szeszko, P., Ardekani, B.A., 2005. Attention-deficit/hyperactivity disorder: a preliminary diffusion tensor imaging study. Biological Psychiatry 57, 448–455. Ashtari, M., Cervellione, K.L., Hasan, K.M., Wu, J., McIlree, C., Kester, H., Ardekani, B.A., Roofeh, D., Szeszko, P.R., Kumra, S., 2007. White matter development during late

Y. Wang et al. / Psychiatry Research: Neuroimaging 202 (2012) 245–251 adolescence in healthy males: a cross-sectional diffusion tensor imaging study. NeuroImage 35, 501–510. Barkley, R.A., 2005. Attention-Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment. Guilford Press, New York. Bava, S., Thayer, R., Jacobus, J., Ward, M., Jernigan, T.L., Tapert, S.F., 2010. Longitudinal characterization of white matter maturation during adolescence. Brain Research 1327, 38–46. Beaulieu, C., 2002. The basis of anisotropic water diffusion in the nervous system—a technical review. NMR in Biomedicine 15, 435–455. Behrens, T.E., Woolrich, M.W., Jenkinson, M., Johansen-Berg, H., Nunes, R.G., Clare, S., Matthews, P.M., Brady, J.M., Smith, S.M., 2003. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine 50, 1077–1088. Burke, J.D., Loeber, R., Birmaher, B., 2002. Oppositional defiant disorder and conduct disorder: a review of the past 10 years, part II. Journal of the American Academy of Child and Adolescent Psychiatry 41, 1275–1293. Bush, G., 2010. Attention-deficit/hyperactivity disorder and attention networks. Neuropsychopharmacology 35, 278–300. Cadoret, R.J., Yates, W.R., Troughton, E., Woodworth, G., Stewart, M.A., 1995. Genetic– environmental interaction in the genesis of aggressivity and conduct disorders. Archives of General Psychiatry 52, 916–924. Casey, B.J., Epstein, J.N., Buhle, J., Liston, C., Davidson, M.C., Tonev, S.T., Spicer, J., Niogi, S., Millner, A.J., Reiss, A., Garrett, A., Hinshaw, S.P., Greenhill, L.L., Shafritz, K.M., Vitolo, A., Kotler, L.A., Jarrett, M.A., Glover, G., 2007. Frontostriatal connectivity and its role in cognitive control in parent–child dyads with ADHD. The American Journal of Psychiatry 164, 1729–1736. Decety, J., Michalska, K.J., Akitsuki, Y., Lahey, B.B., 2009. Atypical empathic responses in adolescents with aggressive conduct disorder: a functional MRI investigation. Biological Psychology 80, 203–211. Dodge, K.A., 1993. Social-cognitive mechanisms in the development of conduct disorder and depression. Annual Review of Psychology 44, 559–584. Findling, R.L., 2008. Atypical antipsychotic treatment of disruptive behavior disorders in children and adolescents. The Journal of Clinical Psychiatry 69, 9–14. Gadow, K.D., Sprafkin, J., 1998. Adolescent Symptom Inventory—4 Norms Manual. Checkmate Plus, Stony Brook, NY. Gioia, G.A., Isquith, P.K., Guy, S.C., Kenworthy, L., 2000. Behavior Rating Inventory of Executive Function proffesional manual. Psychological Assessment Resources, Odessa, FL. Hamilton, L.S., Levitt, J.G., O'Neill, J., Alger, J.R., Luders, E., Phillips, O.R., Caplan, R., Toga, A.W., McCracken, J., Narr, K.L., 2008. Reduced white matter integrity in attentiondeficit hyperactivity disorder. Neuroreport 19, 1705–1708. Herpertz, S.C., Huebner, T., Marx, I., Vloet, T.D., Fink, G.R., Stoecker, T., Shah, N.J., Konrad, K., Herpertz-Dahlmann, B., 2008. Emotional processing in male adolescents with childhood-onset conduct disorder. Journal of Child Psychology and Psychiatry 49, 781–791. Huebner, T., Vloet, T.D., Marx, I., Konrad, K., Fink, G.R., Herpertz, S.C., Herpertz-Dahlmann, B., 2008. Morphometric brain abnormalities in boys with conduct disorder. Journal of the American Academy of Child and Adolescent Psychiatry 47, 540–547. Hummer, T.A., Kronenberger, W.G., Wang, Y., Dunn, D.W., Mosier, K.M., Kalnin, A.J., Mathews, V.P., 2011. Executive functioning characteristics associated with ADHD comorbidity in adolescents with disruptive behavior disorders. Journal of Abnormal Child Psychology 39, 11–19. Kaufman, A.S., Kaufman, N.L., 1990. Kaufman Brief Intelligence Test. American Guidance Service, Circle Pines, NM. Kaufman, J., Birmaher, B., Brent, D., Rao, U., Ryan, N., 1996. Kiddie-SADS-Present and Lifetime Version (K-SADS-PL). Western Psychiatric Institute, Pittsburgh, PA. Konrad, A., Dielentheis, T.F., El Masri, D., Bayerl, M., Fehr, C., Gesierich, T., Vucurevic, G., Stoeter, P., Winterer, G., 2010. Disturbed structural connectivity is related to inattention and impulsivity in adult attention deficit hyperactivity disorder. European Journal of Neuroscience 31, 912–919. Kronenberger, W.K., Meyer, R.G., 2001. The Child Clinician's Handbook, Second ed. Allyn & Bacon, Needham Heights, MA. Kronenberger, W.G., Mathews, V.P., Dunn, D.W., Wang, Y., Wood, E.A., Giauque, A.L., Larsen, J.J., Rembusch, M.E., Lowe, M.J., Li, T.Q., 2005. Media violence exposure and executive functioning in aggressive and control adolescents. Journal of Clinical Psychology 61, 725–737. Li, T.Q., Mathews, V.P., Wang, Y., Dunn, D., Kronenberger, W., 2005. Adolescents with disruptive behavior disorder investigated using an optimized MR diffusion tensor imaging protocol. Annals of the New York Academy of Sciences 1064, 184–192. Ling, J., Merideth, F., Caprihan, A., Pena, A., Teshiba, T., Mayer, A.R., 2012. Head injury or head motion? Assessment and quantification of motion artifacts in diffusion tensor imaging studies. Human Brain Mapping 33 (1), 50–62. Loeber, R., Burke, J.D., Lahey, B.B., Winters, A., Zera, M., 2000. Oppositional defiant and conduct disorder: a review of the past 10 years, part I. Journal of the American Academy of Child and Adolescent Psychiatry 39, 1468–1484.

251

Loeber, R., Burke, J., Pardini, D.A., 2009. Perspectives on oppositional defiant disorder, conduct disorder, and psychopathic features. Journal of Child Psychology and Psychiatry 50, 133–142. Makris, N., Buka, S.L., Biederman, J., Papadimitriou, G.M., Hodge, S.M., Valera, E.M., Brown, A.B., Bush, G., Monuteaux, M.C., Caviness, V.S., Kennedy, D.N., Seidman, L.J., 2008. Attention and executive systems abnormalities in adults with childhood ADHD: a DT-MRI study of connections. Cerebral Cortex 18, 1210–1220. Mathews, V.P., Kronenberger, W.G., Wang, Y., Lurito, J.T., Lowe, M.J., Dunn, D.W., 2005. Media violence exposure and frontal lobe activation measured by functional magnetic resonance imaging in aggressive and nonaggressive adolescents. Journal of Computer Assisted Tomography 29, 287–292. Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A.V., Mahmood, A., Woods, R., Toga, A.W., Pike, G.B., Neto, P.R., Evans, A., Zhang, J., Huang, H., Miller, M.I., van Zijl, P., Mazziotta, J., 2008. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40, 570–582. Nichols, T.E., Holmes, A.P., 2002. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping 15, 1–25. Ollendick, T.H., Jarrett, M.A., Grills-Taquechel, A.E., Hovey, L.D., Wolff, J.C., 2008. Comorbidity as a predictor and moderator of treatment outcome in youth with anxiety, affective, attention deficit/hyperactivity disorder, and oppositional/conduct disorders. Clinical Psychology Review 28, 1447–1471. Oosterlaan, J., Scheres, A., Sergeant, J.A., 2005. Which executive functioning deficits are associated with AD/HD, ODD/CD and comorbid AD/HD+ODD/CD? Journal of Abnormal Child Psychology 33, 69–85. Pavuluri, M.N., Yang, S., Kamineni, K., Passarotti, A.M., Srinivasan, G., Harral, E.M., Sweeney, J.A., Zhou, X.J., 2009. Diffusion tensor imaging study of white matter fiber tracts in pediatric bipolar disorder and attention-deficit/hyperactivity disorder. Biological Psychiatry 65, 586–593. Qiu, D., Tan, L.H., Zhou, K., Khong, P.L., 2008. Diffusion tensor imaging of normal white matter maturation from late childhood to young adulthood: voxel-wise evaluation of mean diffusivity, fractional anisotropy, radial and axial diffusivities, and correlation with reading development. NeuroImage 41, 223–232. Rubia, K., 2011. “Cool” inferior frontostriatal dysfunction in attention-deficit/ hyperactivity disorder versus “hot” ventromedial orbitofrontal-limbic dysfunction in conduct disorder: a review. Biological Psychiatry 69, 68–87. Rubia, K., Halari, R., Smith, A.B., Mohammed, M., Scott, S., Giampietro, V., Taylor, E., Brammer, M.J., 2008. Dissociated functional brain abnormalities of inhibition in boys with pure conduct disorder and in boys with pure attention deficit hyperactivity disorder. The American Journal of Psychiatry 165, 889–897. Rubia, K., Halari, R., Cubillo, A., Mohammad, A.M., Scott, S., Brammer, M., 2010. Disorderspecific inferior prefrontal hypofunction in boys with pure attention-deficit/ hyperactivity disorder compared to boys with pure conduct disorder during cognitive flexibility. Human Brain Mapping 31, 1823–1833. Schmithorst, V.J., Yuan, W., 2010. White matter development during adolescence as shown by diffusion MRI. Brain and Cognition 72, 16–25. Silk, T.J., Vance, A., Rinehart, N., Bradshaw, J.L., Cunnington, R., 2009. White-matter abnormalities in attention deficit hyperactivity disorder: a diffusion tensor imaging study. Human Brain Mapping 30, 2757–2765. Smith, S.M., Nichols, T.E., 2009. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 44, 83–98. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31, 1487–1505. Song, S.K., Sun, S.W., Ramsbottom, M.J., Chang, C., Russell, J., Cross, A.H., 2002. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage 17, 1429–1436. Sterzer, P., Stadler, C., Krebs, A., Kleinschmidt, A., Poustka, F., 2005. Abnormal neural responses to emotional visual stimuli in adolescents with conduct disorder. Biological Psychiatry 57, 7–15. Sterzer, P., Stadler, C., Poustka, F., Kleinschmidt, A., 2007. A structural neural deficit in adolescents with conduct disorder and its association with lack of empathy. NeuroImage 37, 335–342. Walker, L., Chang, L.C., Koay, C.G., Sharma, N., Cohen, L., Verma, R., Pierpaoli, C., 2011. Effects of physiological noise in population analysis of diffusion tensor MRI data. NeuroImage 54, 1168–1177. Wang, Y., Mathews, V., Kalnin, A., Mosier, K., Dunn, D., Saykin, A., Kronenberger, W., 2009. Short term exposure to a violent video game induces changes in frontolimbic circuitry in adolescents. Brain Imaging and Behavior 3, 38–50. Westfall, P.H., Young, S.S., 1993. Resampling-Based Multiple Testing: Examples and Methods for P-Value Adjustment. Wiley, New York.