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research-article2014

JADXXX10.1177/1087054714523316Journal of Attention DisordersDas et al.

Article

Regional Brain Volumes and ADHD Symptoms in Middle-Aged Adults: The PATH Through Life Study

Journal of Attention Disorders 1­–14 © 2014 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054714523316 jad.sagepub.com

Debjani Das1, Nicolas Cherbuin1, Kaarin J. Anstey1, Walter Abhayaratna1,2, and Simon Easteal1

Abstract Objective: We investigated whether volumetric differences in ADHD-associated brain regions are related to current symptoms of inattention and hyperactivity in healthy middle-aged adults and whether co-occurring anxiety/depression symptoms moderate these relationships. Method: ADHD Self-Report Scale and Brief Patient Health Questionnaire were used to assess current symptoms of inattention, hyperactivity, anxiety, and depression in a population-based sample (n = 269). Brain volumes, measured using a semi-automated method, were analyzed using multiple regression and structural equation modeling to evaluate brain volume–inattention/hyperactivity symptom relationships for selected regions. Results: Volumes of the left nucleus accumbens and a region overlapping the dorsolateral prefrontal cortex were positively associated with inattention symptoms. Left hippocampal volume was negatively associated with hyperactivity symptoms. The brain volume–inattention/hyperactivity symptom associations were stronger when anxiety/depression symptoms were controlled for. Conclusion: Inattention and hyperactivity symptoms in middle-aged adults are associated with different brain regions and co-occurring anxiety/depression symptoms moderate these brain–behavior relationships. (J. of Att. Dis. XXXX; XX(X) XX-XX) Keywords inattention, hyperactivity, depression, middle age, population-based sample, MRI, prefrontal cortex, accumbens, hippocampus

Introduction ADHD is characterized by age-inappropriate levels of inattention and/or excessive motor activity and impulsivity (Biederman & Faraone, 2005). It is now widely accepted that ADHD may persist into adulthood. However, studies on middle-age and older adults are relatively few. Prevalence estimates suggest that ADHD is a common disorder affecting ~5% of children (a recent study reported much higher prevalence; Visser et al., 2014) and 1% to 7% of adults (Biederman, Mick, & Faraone, 2000; Ebejer et al., 2012; Faraone, Biederman, Spencer, et al., 2006; Fayyad et al., 2007; Polanczyk & Rohde, 2007; Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007). Inattention is the predominant symptom in adults, which manifests as disorganization, forgetfulness, and poor performance in planning, task completion, task shifting, and time management. These symptoms often lead to employment and financial difficulties and interpersonal problems (Barkley, 2010; Karam et al., 2009). Individuals with ADHD are more likely to experience comorbid psychiatric disorders (Karam et al., 2009; Rösler, Casas, Konofal, & Buitelaar, 2010). The majority of adults with ADHD have at least one lifetime

psychiatric comorbidity including anxiety (47%), mood (38%), impulse control (20%), and substance use disorders (15%; Kessler et al., 2006). Taking account of direct medical expenses and indirect costs associated with workplace productivity loss and accidents, the economic impact of ADHD is substantial (Bernfort, Nordfeldt, & Persson, 2008; Birnbaum et al., 2005; Matza, Paramore, & Prasad, 2005; Secnik, Swensen, & Lage, 2005; Swensen et al., 2003). To understand the biological underpinnings of the disorder, the neurobiological characteristics of ADHD have been investigated using neuroimaging techniques. At the group level, brains of ADHD patients and unaffected individuals appear to be different. These differences are presumed to underlie the disorder and associated cognitive and emotional disturbances. While relatively few, neuroimaging 1

Australian National University, Canberra, Australia Canberra Hospital and Health Services, Australia

2

Corresponding Author: Debjani Das, John Curtin School of Medical Research, Australian National University, Building 131, Garran Road, Canberra, Australian Capital Territory 2600, Australia. Email: [email protected]

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studies in adults indicate that ADHD-related brain abnormalities persist into adulthood (Cubillo & Rubia, 2010). These studies reported that adult ADHD patients have reduced overall cortical gray matter; decreased volumes of the dorsolateral prefrontal cortex, anterior cingulate cortex (Amico, Stauber, Koutsouleris, & Frodl, 2011; Seidman et al., 2006), caudate (Almeida Montes et al., 2010; Seidman et al., 2011), and amygdala (Frodl et al., 2010); as well as larger nucleus accumbens volume (Seidman et al., 2006). A previous study found that several cortical and subcortical regions were either smaller or larger in ADHD adults but only the caudate remained significantly smaller after correcting for multiple comparisons (Seidman et al., 2011). Significant cortical thinning in the inferior parietal lobule, dorsolateral prefrontal, and cingulate cortices has also been reported (Makris et al., 2007). A negative association between total brain volume and self-reported ADHD symptoms (dimensional measure) (Hoogman et al., 2012) was found in young adults (age range = 18-35 years), although this had not been observed in other studies (Amico et al., 2011; Hesslinger et al., 2002; Seidman et al., 2006). The limited number of studies and the use of different methodologies mean that few have been confirmed by replication. Symptoms of ADHD, particularly inattention, are common with nearly 60% of the adult population displaying some level of symptoms (Arcos-Burgos & Acosta, 2007). Individuals with few symptoms who do not meet the full diagnostic criteria also suffer significant functional impairment (Faraone, Biederman, Doyle, et al., 2006; Faraone, Biederman, Spencer, et al., 2006; Karam et al., 2009). There is increasing recognition that clinical ADHD lies at the extreme ends of the distribution of continuous ADHD symptom dimensions. Accumulating evidences from taxonomic, genetic, and neuroimaging studies support the dimensional view (Lubke, Hudziak, Derks, van Bijsterveldt, & Boomsma, 2009; Nikolas & Burt, 2010; Shaw et al., 2011). The majority of these studies are of childhood ADHD, but a recent taxonomic study in an adult sample also indicates a dimensional latent structure of ADHD (Marcus, Norris, & Coccaro, 2012). Inattention and hyperactivity are the two major dimensions of ADHD (American Psychiatric Association [APA], 2000), which are associated with distinct brain and cognitive characteristics (Carr, Henderson, & Nigg, 2010; Fair et al., 2012; Solanto, Schulz, Fan, Tang, & Newcorn, 2009). Recent studies have also identified executive functioning and impulsivity as dimensions of adult ADHD symptomatology (Kessler et al., 2010; McKee, 2012). As the majority of neuroimaging studies used clinical samples, it is not known whether subclinical symptom levels are similarly associated with brain structural differences. Furthermore, the neural correlates of ADHD symptom dimensions have not been systematically investigated in middle-age or older adults.

Focusing on two major dimensions of ADHD—inattention and hyperactivity—we have previously investigated the effect of these symptom dimensions in a general population of middle-age and older adults. Using self-reported measures of inattention and hyperactivity symptoms in healthy middle-age adults participating in the Personality and Total Health (PATH) Through Life Study (Anstey et al., 2012), we reported that greater number and/or frequency of symptoms (henceforth referred to as symptom levels) were associated with functional impairments (including health, work, relationship, social interactions, and well-being; Das, Cherbuin, Butterworth, Anstey, & Easteal, 2012) and worse performance in cognitive tests (Das, Cherbuin, Anstey, & Easteal, 2012). Individuals with moderate symptoms levels (i.e., that are likely to be below the diagnostic threshold) are behaviorally and cognitively different from those with few or no symptoms. Effects of inattention and hyperactivity symptoms on cognitive and behavioral measures are dissociable and often reciprocal. Furthermore, we found moderating effects of co-occurring anxiety/depression symptoms suggesting that behavioral/cognitive characteristics of ADHD might differ based on comorbid conditions. We also recently reported that older participants (aged 68-74 years) in the PATH study have significantly lower levels of inattention and hyperactivity symptoms than middle-age adults (Das, Cherbuin, Easteal, & Anstey, 2014). In this study, we investigate relationships between brain structure (total and regional) and symptoms of inattention and hyperactivity in healthy middle-aged adults. To assess the extent to which structural brain differences might be best explained by the presence of mood disorders, we also investigate whether brain volume–inattention/hyperactivity symptom relationships are moderated by anxiety and depression symptoms. We conduct a region of interest (ROI) analysis with brain regions selected a priori, based on the findings from the previous neuroimaging studies in adults with ADHD. The ROIs include bilateral dorsolateral prefrontal and anterior and posterior cingulate cortices, caudate, nucleus accumbens, and amygdala (Figure 1). Hippocampal volume is also included as a ROI as a previous study on the PATH sample found significant associations between total hippocampal volume and behavioral inhibition (Cherbuin et al., 2008), which has been suggested as a core feature of ADHD (Barkley, 1997). Studies have shown that deficient response inhibition is a primary deficit in ADHD (Heilman, Voeller, & Nadeau, 1991; Wodka et al., 2007). The present study was conducted in a population-based sample of middle-aged adults using high-resolution structural images of the brain and self-reported current symptoms of inattention and hyperactivity, anxiety, and depression. We hypothesized that interindividual differences in volumes of these brain regions would be significantly correlated with differences in inattention/hyperactivity symptom levels in middleaged adults.

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Figure 1.  3-D model of the human brain superimposed on the MRI image of head indicating the brain regions analyzed in this study. (A) Lateral view. (B) Medial view.

Method Participants The study sample was drawn from the middle-age cohort of the PATH Through Life Project, a longitudinal study of mental health and aging in participants across three age groups (20-24, 40-44, 60-64 years at baseline) with fouryearly follow-up “waves” of assessment for up to 20 years (Anstey et al., 2012). For the middle-age cohort, the baseline sample included individuals (n = 2,530) selected randomly from the electoral roll from the city of Canberra and the adjacent town of Queanbeyan, Australia (which provides a representative population sample because enrollment to vote is a legal requirement for adult Australian citizens). Written informed consent for participation in the PATH project was obtained from all participants according to the “National Statement” guidelines of the National Health and Medical Research Council of Australia and following a protocol approved by the Human Research Ethics Committee of The Australian National University. In the second wave of assessment, a randomly selected sub-sample of 656 was offered a Magnetic Resonance Imaging (MRI) scan, which 431 completed. In all, 300 participants subsequently returned for a second MRI scan during the third wave of assessment. Collection of MRI scans was completed over 7 months (August 2008-March 2009). The present study used data from the third wave since the Adult ADHD Self-Report Scale (ASRS) was introduced in this wave. A total of 31 scans were excluded from the study due to poor scan quality (n = 3), missing data (n = 12), history of epilepsy (n = 1), brain tumor (n = 2), brain infection (n = 8), and stroke (n = 5) leaving 269 scans (126 males and 143 females; age = 48-52 years) available for analysis. There were no significant differences between sub-samples with and without MRI data with respect to socio-demographic

variables and symptoms of ADHD, anxiety, or depression. Further details of the ASRS measure for the whole cohort are available in Das, Cherbuin, Butterworth, et al. (2012). (Information about access to data is available at http://crahw .anu.edu.au/research/projects/personality-total-health-paththrough-life/data.)

Measures Current inattention and hyperactivity symptoms were assessed using the ASRS (short-form). The ASRS is a selfreport measure of ADHD developed for the World Health Organization (WHO) World Mental Health Initiative surveys. The six items in the short-form of the screener was selected by step-wise logistic regression from a pool of 18 questions, which corresponded to the 18 criteria A symptoms of ADHD in the Diagnostic and Statistical Manual of Mental Disorders IV (4th ed.; DSM-IV; APA, 1994; Kessler et al., 2007, 2005). The six-question ASRS screener was reported to outperform the 18-question ASRS in sensitivity, specificity, total classification accuracy, and concordance with clinical rating (Kessler et al., 2005). In the ASRS (short-form), the first four questions assess inattention and the last two are related to hyperactivity. Each item requires participants to rate on a 5-point response scale from “never” [0] to “very often” [4] how frequently a particular symptom occurred over the past 6 months. A summary score (ASRSscore) with a possible range of 0 to 24 was obtained as an equally weighted sum of response scores for all questions. Higher scores indicate increased risk of ADHD (Kessler et al., 2005, 2007). This screening tool has performed well in validation studies (sensitivity = 68.7% and specificity = 99.5%) and has high concordance with clinician diagnosis (area under the receiver operator curve of 0.90; Kessler et al., 2007). Factor analysis of the ASRS reported

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previously (Hesse, 2013) suggests that the screener is a two-dimensional rather than one unitary measure. Items 1 to 4 related to inattention symptoms load on one factor (inattentiveness) and Items 5 and 6 load on a second factor (hyperactivity). Hence, for our investigation of the effects of inattention and hyperactivity dimensions, we generated an equally weighted sum of response scores for the inattention symptom items (Inattention-score) and the hyperactivity items (Hyperactivity-score). The distribution of Inattention-score and Hyperactivity-score for the entire sample has been reported previously (Das, Cherbuin, Butterworth, et al., 2012) and were not significantly different in the imaging sub-sample. Anxiety and depression symptoms were assessed using the Patient Health Questionnaire (PHQ). This is a short version of the patient questionnaire component of the Primary Care Evaluation of Mental Disorders (PRIME-MD) instrument (Martin, Rief, Klaiberg, & Braehler, 2006; Spitzer, 1999). We generated measures of depression symptoms (DEP) and anxiety-related symptoms (ANX) from the nine items related to depression (rated on a 4-point scale from “not at all” [1] to “nearly every day” [4]), seven items related to anxiety (rated on a 3-point scale from “not at all” [1] to “more than half the days” [3]), and five items related to panic disorder (rated on a 2-point scale of “no” [1] and “yes” [2]) following the coding algorithm provided in the PHQ instruction manual (available from Patient Health Questionnaire Screeners; http://www.phqscreeners.com/ overview.aspx). Variables for panic disorder and other anxiety-related conditions were combined to generate a binary categorical variable (scored as “0” if both panic disorder and other anxiety syndrome are absent, and scored as “1” if symptoms of either panic disorder or other anxiety-related conditions are present).

MRI data were acquired on a 1.5 Tesla Gyroscan scanner (ACS-NT, Philips Medical Systems, Best, The Netherlands). T1-weighted 3-D structural MRI images were acquired in coronal plane using Fast Field Echo (FFE) sequence. The scanning parameters were TR = 8.84 ms, TE = 3.55 ms, a flip angle of 8°, matrix size = 256 × 256, slices 160, and the field of view (FOV) 256 × 256 mm. Slices were contiguous with a thickness of 1.5 mm.

test−retest reliability across scanner manufacturers and across field strengths (Han, Jovicich, Salat, van der Kouwe, & Quinn, 2006; Reuter, Schmansky, Rosas, & Fischl, 2012). Automated identification of ROIs using FreeSurfer has been validated against manual tracing in several studies and shown to perform better than other available tools (Cherbuin, Anstey, Reglade-Meslin, & Sachdev, 2009; Desikan et al., 2006; Dewey et al., 2010; Morey et al., 2009; Shen et al., 2010). The image processing steps include motion correction and averaging (Reuter, Rosas, & Fischl, 2010), removal of non-brain tissue using a hybrid watershed/surface deformation procedure (Ségonne, Dale, Busa, Glessner, & Salat, 2004), automated Talairach transformation, and segmentation of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, and ventricles; Fischl et al., 2002, 2004), intensity correction, and delineation of gray/white/cerebrospinal fluid boundaries (Fischl & Dale, 2000). Following completion of cortical models, a number of deformable procedures were applied including surface inflation (Fischl, Sereno, & Dale, 1999), registration to a spherical atlas (Fischl, Sereno, Tootell, & Dale, 1999), and parcellation of the cerebral cortex into neuroanatomical units based on gyral and sulcal structure (Desikan et al., 2006). The segmentation and parcellation processes use probabilistic information estimated from the manually labeled training set to automatically assign a neuroanatomical label to each voxel. This procedure was shown to be both anatomically valid and highly reliable (Desikan et al., 2006). Each processed scan was inspected slice by slice and reprocessed if errors in segmentation were identified and excluded if errors were not correctable. It is important to note that there are several different methods available for partitioning the brain into cortical and subcortical neuroanatomical structures but there is currently no accepted standard. Furthermore, there is a lack of concordance between brain atlases used for generating labels for the partitioned regions. Consequently, ROIs identified using these different methods are not always exactly equivalent, which poses a challenge for comparing ROIs across studies (Bohland, Bokil, Allen, & Mitra, 2009). As FreeSurfer is the most commonly used semi-automated tool, it provides the best opportunity to produce results that can be compared across studies.

Image Analysis

Brain Volume Measures

Volumetric segmentation was performed with the FreeSurfer image analysis suite (http://surfer.nmr.mgh.harvard.edu/), which is a highly regarded and freely available set of tools for deriving neuroanatomical volumes and cortical thickness measurements from MRI scans. FreeSurfer morphometric procedures have been demonstrated to show good

Total brain volume and volumes of two cortical and four subcortical regions (which have been implicated in ADHD in previous research) were analyzed. These ROIs included the dorsolateral prefrontal and cingulate cortices, caudate, nucleus accumbens, hippocampus, and amygdala. The FreeSurfer regions most coincident with the dorsolateral

MRI Scan Acquisition

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Das et al. prefrontal cortex are superior frontal and rostral and caudal middle frontal regions. For the cingulate cortex, they include rostral and caudal anterior cingulate cortex and posterior cingulate cortex. The nucleus accumbens is included in the region labeled accumbensarea. The cortical and subcortical ROIs analyzed are illustrated in Figure 1. Neuroanatomical regions identified by different brain atlases are not always concordant so, for clarity, we refer to the brain ROIs using the labels available in FreeSurfer.

Statistical Analysis Statistical analyses were conducted using SPSS version 18 and Amos version 20 (Chicago: SPSS Inc.). Total brain and ROI volumes were adjusted for intracranial volume (ICV) to control for head size using the formula: adjusted volume = raw volume − b × (ICV − mean ICV) where b is the slope of regression of an ROI volume on ICV. Means and standard deviations were computed for age, total years of education, ADHD symptom scores, and brain volumes. Student’s t tests were used to compare ADHD symptom scores between groups with and without anxiety/depression symptoms. Pearson correlations were computed for ROI volumes of the left and right hemisphere, total brain volume, and ADHD symptom scores (Table S1). ROI volumes that were correlated with Inattention-score, Hyperactivity-score, and ASRS-score at the p ≤ .1 level were selected for further analyses using multiple regression. Separate regression models were generated for predicting Inattention-score, Hyperactivity-score, or ASRS-score. Age, sex, total years of education, ANX, and DEP were covariates in the models as indicated. Regression models were generated by entering covariates first, followed by the predictors (ROI volumes). Change in R2 value between each step and the p value associated with R2 change was noted in addition to the standardized coefficients and p values for each predictor variable. Models were reduced by removing predictors with p value > .1. Results are presented at significant thresholds of p ≤ .01 and p ≤ .05.

Results Table 1 lists demographic characteristics and mean ADHD symptom scores and ROI volumes. Mean symptom scores and their distribution in the sub-sample with MRI data did not differ significantly (p > .2) from those reported for the entire cohort (Das, Cherbuin, Butterworth et al., 2012). Both ASRS-score and the core dimension scores (Inattentionscore and Hyperactivity-score) were significantly higher in participants with anxiety symptoms (Table 2). However, only ASRS-score and Inattention-score were significantly higher in participants with depressive symptoms (Table 2). From zero-order correlations, we identified eight ROIs that were potentially associated with the symptom scores.

Table 1.  Demographic Characteristics, Inattention and Hyperactivity Symptom Scores, and Brain Volumes of the Sample.

Age (years) Education (years) Inattention-score Hyperactivity-score ASRS-score TBV (mL) Region of interest volumes (mL)   L superior frontal   R superior frontal   L rostral middle frontal   R rostral middle frontal   L caudal middle frontal   R caudal middle frontal   L rostral anterior cingulate   R rostral anterior cingulate   L caudal anterior cingulate   R caudal anterior cingulate   L posterior cingulate   R posterior cingulate   L caudate   R caudate   L accumbens   R accumbens   L hippocampus   R hippocampus   L amygdala   R amygdala

M

SD

50.72 14.93 5.46 2.67 8.13 1,595.08

1.43 2.20 2.41 1.68 3.21 8.30

22.70 21.48 14.75 15.89 6.45 6.07 2.30 1.86 1.76 2.11 3.03 3.22 3.49 3.47 0.52 0.57 4.39 4.43 1.56 1.63

2.07 2.05 1.77 1.74 1.09 1.18 0.42 0.39 0.44 0.44 0.41 0.41 0.36 0.36 0.07 0.07 0.31 0.30 0.15 0.15

Note. All brain volumes are in milliliters and are adjusted for intracranial volume. ASRS = Adult ADHD Self-Report Scale; TBV = total brain volume; L = left; R = right.

These ROIs were analyzed further using multiple regression with age, sex, and education included as covariates. When predicting Inattention-score, Hyperactivity-score was included as an additional covariate and vice versa. At the threshold of p ≤ .01, we did not observe any statistically significant association between brain volumes and Inattention-score, Hyperactivity-score, or ASRS-score. However, few associations were significant at p ≤ .05, which are presented below.

ROIs Associated With Inattention-Score Zero-order correlations between two ROI volumes and Inattention-score were significant at p ≤ .1 (Table S1). This included the rostral middle frontal and accumbensarea regions of the left hemisphere. Inattention-score was associated (positively) with the left accumbensarea volume (β = 0.118, p = .047) and a trend (positive) was observed for the left rostral middle frontal volume (β = 0.104, p = .079).

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Table 2.  Group Differences in ADHD Symptom Scores Based on Presence of Anxiety/Depression Symptoms. Inattention-score  

M (SD)

DEP   Absent (n = 239)   Present (n = 30) ANX   Absent (n = 256)   Present (n = 13)

Hyperactivity-score

t

df

−6.175***

267

M (SD)

5.16 (2.18) 7.87 (2.82)

ASRS-score

t

df

−1.616*

267

2.61 (1.67) 3.13 (1.53) −3.358***

t

df

−5.456***

267     267    

7.77 (3.02) 11.00 (3.34)

267

−4.255***

5.36 (2.34) 7.62 (2.82)

M (SD)

267

2.57 (1.63) 4.54 (1.45)

−4.813*** 7.93 (3.08) 12.15 (3.14)

Note. ASRS = Adult ADHD Self-Report Scale; DEP = depression symptom measure; ANX = anxiety symptom measure *p > .05. ***p ≤ .001.

Table 3.  Associations Between ADHD Symptom Scores and Brain Volumes. Model 1

Model 2

Full

  β

 

Full p

β

Reduced p

β

p

a

Inattention-score   L rostral middle frontal   L accumbens   Hyperactivity-scoreb   L superior frontal   R rostral anterior cingulate   R posterior cingulate   R accumbens   L hippocampus   ASRS-score   L superior frontal   R caudal anterior cingulate  

0.103 .081 0.117 .048* R2 (change) = .099 (.024)

0.132 .013* 0.128 .016* R2 (change) = .284 (.034)

     

−0.047 .495 −0.054 .395 −0.079 .224 −0.054 .405 −0.097 .130 R2 (change) = .088 (.038)

−0.045 .470 −0.048 .436 −0.059 .350 −0.037 .557 −0.120 .057 R2 (change) = .104 (.035)

        −0.146 .013* R2 (change) = .104 (.021)

−0.094 .132 −0.067 .289 R2 (change) = .034 (.016)

−0.041 .459 −0.049 .377 R2 (change) = .241 (.005)

     

Note. All brain volumes were adjusted for intracranial volume. Model 1 controlled for age, sex, and education. Model 2 controlled for age, sex, education, and anxiety and depression symptom scores. ASRS = Adult ADHD Self-Report Scale; L = left; R = right. *p < .05. a Controlled for Hyperactivity-score. b Controlled for Inattention-score.

When both ROIs were entered in the regression model together, the associations remained practically unchanged (Table 3: Model 1). For both ROIs, a larger volume is associated with greater inattention symptoms. Total brain volume was not significantly associated with Inattention-score.

ROIs Associated With Hyperactivity-Score Significant zero-order correlations were detected between Hyperactivity-score and volumes of the superior frontal region and hippocampus of the left hemisphere and rostral anterior cingulate, posterior cingulate, and accumbensarea of the right hemisphere (Table S1). In regression analysis,

Hyperactivity-score was negatively associated with left hippocampal volume (β = −0.128, p = .032). Associations between Hyperactivity-score and left superior frontal (β = −0.102, p = .092) and right rostral anterior cingulate (β = −0.099, p = .100) volumes were not statistically significant. Trends were observed for right posterior cingulate (β = −0.118, p = .051) and accumbensarea (β = −0.111, p = .065) volumes. In all cases, smaller volumes of these ROIs were associated with greater hyperactivity symptoms. However, none of the ROIs was significantly associated with Hyperactivity-score when entered in the model together (Table 3: Model 1). Total brain volume was not significantly associated with Hyperactivity-score.

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ROIs Associated With ASRS-Score

Discussion

Zero-order correlations between ASRS-score and left superior frontal and right caudal anterior cingulate volumes were significant (Table S1, available online at jad.sagepub .com/supplemental). ASRS-score was not significantly associated with the right caudal anterior cingulate volume (β = −0.086, p = .162) and only a trend was observed for the left superior frontal volume (β = −0.108, p = .078) in multiple regression analysis while controlling age, sex, and education. Here too, smaller volumes were associated with greater symptoms. These ROIs were not significant predictors of ASRS-score when entered in the regression model together (Table 3: Model 1). Total brain volume was not significantly associated with ASRS-score.

In this study, we investigated brain volume–inattention/ hyperactivity symptom relationships in a sample of healthy, middle-aged adults and the moderating effect of anxiety and depression symptoms on these relationships. The study was conducted in an epidemiological sample (which is well suited for investigation on inattention and hyperactivity dimensions) with a narrow age cohort design for minimizing any age-related brain differences. The main findings of the study are as follows: (a) current inattention and hyperactivity symptoms in healthy middle-aged adults are associated with volumetric differences in specific brain regions, (b) ROI volumes were found to be selectively associated with either Inattention-score or Hyperactivity-score, but not with both, (c) greater Inattention-score is associated with larger ROI volumes of the left rostral middle frontal region and the accumbensarea, (d) smaller left hippocampal volume was associated with Hyperactivity-score, (e) co-occurring symptoms of anxiety and depression moderate but do not mediate the relationship between ROI volumes and Inattention-score/Hyperactivity-score. Inattention-score was positively associated with volumes of the rostral middle frontal region (which overlaps with the dorsolateral prefrontal cortex) and accumbensarea of the left hemisphere. Together these two volumes explained ~3% of the variance in Inattention-score. The dorsolateral prefrontal cortex has been implicated in “topdown” control of attention, particularly when it has to be sustained over time (Banich, Milham, Atchley, & Cohen, 2000; Milham, Banich, & Barad, 2003). Studies have shown that the dorsolateral prefrontal cortex is important for both reactive and proactive attentional control, particularly in the active maintenance and updating of current goals, and use of this to adjust behavior (Braver & Barch, 2006; Braver, Gray, & Burgess, 2007; Braver, Paxton, Locke, & Barch, 2009). It is also critical for bimodal divided attention (Johnson, Strafella, & Zatorre, 2007). Hence, an association between this region and inattention symptoms is consistent with existing evidence. However, it is not clear how the volume of this brain region affects regulation of attention. Two previous studies (Seidman et al., 2011, 2006) in ADHD patients found divergent results for the dorsolateral prefrontal cortex using different methodologies. The first study (Seidman et al., 2006) reported a decrease in the left hemispheric volume, but the subsequent study (Seidman et al., 2011) reported a smaller volume for the left and a larger volume for the right hemisphere (although these results were not significant after multiple test correction). Thus, the exact nature of the relationship between the dorsolateral prefrontal cortex and inattention is unclear and needs to be addressed in future studies. Experimental studies in animals have demonstrated that the nucleus accumbens plays an important role in

Moderating Effect of Anxiety and Depression Symptoms To investigate whether the brain volume–inattention/ hyperactivity symptom relationships we identified were affected by anxiety/depression symptoms, ANX and DEP were included as covariates in the regression models predicting Inattention-score, Hyperactivity-score, or ASRS-score (Table 3: Model 2 and Figure 2). For Inattention-score and Hyperactivity-score, the associations with the ROI volumes were stronger when anxiety and depression symptoms were controlled for. These results indicate a moderating effect of anxiety and depression symptoms on brain volume–inattention/ hyperactivity symptom relationships. However, such a moderating effect was not observed for ASRS-score (Table 3). To further evaluate these relationships, we used structural equation modeling (SEM), which allows all ROIs to be assessed as predictors of Inattention-score and Hyperactivity-score in a single model (Figure S1). In the SEM model, we examined whether ANX and DEP mediate brain volume–inattention/hyperactivity symptom relationships. Details of the methodology, results, and schematic representations of models are provided as supplementary data (available at jad.sagepub.com/supplemental). ROIs not significantly associated with either trait score were progressively removed to refine the SEM model. In the final parsimonious model, volumes of the left rostral middle frontal region and the accumbensarea were positively associated with Inattention-score and left hippocampal volume was negatively associated with Hyperactivity-score (Table S2 and Figure S2). This model explained 28% and 12% of the variance in Inattention-score and Hyperactivity-score, respectively. There were no direct significant paths from ROI volumes to ANX and DEP indicating that these variables do not mediate the brain volume–inattention/ hyperactivity symptom relationships. The SEM results are nearly identical to that obtained in the multiple regression analyses of selected ROIs (Table 3).

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Figure 2.  Partial regression plots (Panels A-C) displaying correlations between residuals of Inattention-score (Panels A and B) or Hyperactivity-score (Panel C) and residuals of ROI volumes adjusting for effects of age, sex, education, and symptoms of anxiety and depression. Panel D is a 3-D model of the human brain superimposed on the MRI image of head indicating ROIs (lateral view) significantly associated with inattention/hyperactivity symptoms.

Note. “+” indicates positive association with symptoms of inattention and “−” indicates negative association with hyperactivity symptoms. ROI = region of interest.

attentional processes (Jongen-Rêlo, Kaufmann, & Feldon, 2002; Pezze, Dalley, & Robbins, 2007). Dopaminergic innervation of the nucleus accumbens is critical for latent inhibition, a process related to selective attention (Young, Moran, & Joseph, 2005). Thus, our report of a significant association between accumbensarea volume and Inattentionscore aligns with the known function of the accumbens in attentional control. However, it is not obvious how the relatively large size of the accumbens region affects attentional processes although the positive association reported here is consistent with a previous study in ADHD adults (Seidman et al., 2006). The nucleus accumbens also has a central role

in reward mechanisms (Blum et al., 2000) and there is evidence for dysfunction in reward and motivation in adults with ADHD (Johansen et al., 2009). Moreover, abnormal accumbens activity has been associated with repetitive behavior observed in obsessive compulsive disorder (Figee et al., 2013), which could potentially relate to hyperactivity symptomatology in ADHD. However, our results appear to be more consistent with accumbens dysfunction being related to symptoms of inattention than to hyperactivity. We found a significant negative association between the left hippocampal volume and hyperactivity symptoms with the ROI explaining ~2% of the variance in Hyperactivity

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Das et al. score. Thus, a larger hippocampus correlates with lower symptom levels. The direct relationship between hippocampus and activity is well supported from animal studies and hippocampal lesions in rodents reliably produce locomotor hyperactivity (Bast & Feldon, 2003). Our result is also in line with a previous study on the middle-age cohort of PATH where total hippocampal volume was positively associated with behavioral inhibition (Cherbuin et al., 2008). Studies in children demonstrate that deficient response inhibition is a primary deficit in ADHD (Heilman et al., 1991; Wodka et al., 2007). We found that hippocampal volume is associated with hyperactivity but not inattention symptoms, suggesting that behavioral inhibition is more related to hyperactivity than inattention. Three previous studies (Amico et al., 2011; Perlov et al., 2008; Seidman et al., 2006) reported a lack of significant association between hippocampal volume and ADHD while another (Seidman et al., 2011) found a smaller volume of the left hippocampus in ADHD adults (which did not survive multiple testing correction). Our finding that hippocampal volume is associated with Hyperactivity-score but not with ASRS-score suggests that hippocampal differences might be present only in the hyperactive subtype of ADHD. Hence, significant differences in this structure might not be detectable when all subtypes are analyzed together. This possibility, however, remains to be tested. Several factors including the neuronal and non-neuronal cell number and size and dendritic and axonal architecture may result in a relatively larger or smaller size of a specific brain region as detected in MRI scans. Given the limited resolution of MR images, it is not possible to determine the specific biological process underlying the volumetric differences or its effect on neural function from imaging data. Furthermore, due to the cross-sectional nature of the study, we cannot determine whether the volumetric differences we identified have a developmental origin or are due to aging processes or both. The effect of age is not uniform across the brain and the three ROIs identified in the study are among the regions that are more sensitive to the aging process. Both cross-sectional and longitudinal imaging studies have demonstrated progressive loss of in the volumes of the prefrontal cortex, hippocampus, and accumbens with age (Goodro, Sameti, Patenaude, & Fein, 2012; Raz, 2000, 2004; Raz et al., 1997; Walhovd et al., 2005). Thus, if the rostral middle frontal, accumbal, and the hippocampal volumes are causally related to inattention and hyperactivity symptoms, respectively, then it is plausible that age-related brain changes might reduce inattention symptoms but exacerbate hyperactivity symptoms. However, in the old-age cohort of the PATH study, participants report significantly lower levels of both inattention and hyperactivity symptoms (Das et al., 2014). This suggests that the relationships between regional brain volume and symptoms of inattention and hyperactivity are complex and might change across

the life span. Further studies are required to investigate these issues and longitudinal follow-up of the sample might aid in clarifying these relationships. We found significant brain volume−inattention symptom associations only in the left hemisphere. Clinical studies of patients with brain lesions suggest dominance of the right hemisphere on attention control (Mesulam, 1999). Right hemisphere dysfunction has been implicated in ADHD (Stefanatos & Wasserstein, 2001); however, with respect to neurobiological substrates, the evidence for this is mixed. Thus, it is possible that the structural differences in the left hemisphere we detected could interfere with right hemispheric dominance that is characteristic of normal attention. Moreover, defect in the corpus callosum (which has been reported in ADHD patients; Dramsdahl, Westerhausen, Haavik, Hugdahl, & Plessen, 2012) could affect interhemispheric communication thereby accentuating the imbalance between right and left hemispheric control of attention. We observed differences in ROI volume−inattention/ hyperactivity symptom relationships based on whether or not symptoms of anxiety and depression were controlled for (Table 3). Our results suggest that ROI volumes are better predictors of Inattention-score and Hyperactivity-score when the variance explained by anxiety and depression symptoms has been accounted for. However, the ROI volumes were not directly associated with symptoms of anxiety and depression. This suggests that while symptoms of mood disorder are strongly correlated with inattention and hyperactivity symptom and moderate the ROI volumeinattention/hyperactivity symptom relationships, they do not mediate the effect of volumetric differences on inattention and hyperactivity symptoms. Whether this is also true for clinical ADHD cases, as comorbidity between ADHD and mood disorders is common (Barkley, 2010), needs to be investigated in future studies. A previous study compared brain volume in a small group of patients with ADHD and major depressive disorder and healthy controls and reported that amygdala volumes bilaterally were smaller in ADHD patients compared with the other groups (Frodl et al., 2010). However, the study did not address whether amygdala volume was different between ADHD patients with and without depression. Our analyses indicate a lack of association between ADHD symptoms and amygdala volume in either hemisphere irrespective of adjustments for anxiety/depression symptoms. We observed significant associations between ROI volumes and Inattention-score/Hyperactivity-score but not with the composite ASRS-score. Moreover, Inattentionscore and Hyperactivity-score were associated with different brain regions. These results indicate that inattention and hyperactivity symptoms have distinct brain−behavior relationships. Significant associations were observed for only a few of the ROIs examined, suggesting that only some volumetric differences previously associated with ADHD

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correlate with the ADHD symptom dimensionality. Other reported volumetric differences might be present only at the extreme of the distribution and were not detected in this study because it includes the full range of symptom scores. We did not find a significant association between total brain volume and inattention symptoms such as that previously reported in a non-clinical sample of young adults (Hoogman et al., 2012). However, our result is consistent with previous reports of a lack of association between ADHD diagnosis and whole brain measures in patients (Amico et al., 2011; Hesslinger et al., 2002; Seidman et al., 2006). Further study is needed to clarify inconsistencies between these different studies. The strengths of this study are that it was conducted in a representative population sample and is hence less likely to be affected by biases that can be associated with clinical and convenience samples. The narrow age range of the sample greatly minimizes the possible confounding effects of age. Consequently, our results are likely to be generalizable to middle-aged populations. Our study addresses the confounding effect of co-occurring ADHD and mood disorder symptoms by clearly demonstrating how brain volume−inattention/hyperactivity symptom relationships are affected by the inclusion or exclusion of depression/ anxiety symptoms. To the best of our knowledge, this is the first study to evaluate brain−behavior relationships with respect to inattention and hyperactivity symptoms in healthy middle-aged adults. The limitations of the study are that symptom measures are based on self-reports, which may not be completely accurate (e.g., social desirability and current emotional state could introduce biases; Brewin, Andrews, & Gotlib, 1993; Neugebauer & Ng, 1990). However, the assessment instruments we used have been shown to have good sensitivity and specificity and they have been used in several previous studies and validated in different cultural settings (Kessler et al., 2007; Manea, Gilbody, & McMillan, 2012; Martin et al., 2006; Ramos-Quiroga et al., 2009). Other dimensions of ADHD—executive functioning and impulsivity—were not included in this study. We have reported p values that were not corrected for multiple comparisons. Nevertheless, the associations identified in multiple regression analysis were identical to those obtained using SEM, which is a more robust method that allows simultaneous analysis of all variables in the model. We could not ascertain the cause−effect relationship between symptoms of inattention, hyperactivity, depression, and anxiety due to the crosssectional nature of the study. Despite these limitations, our study provides evidence that in healthy middle-aged adults, volumetric differences in specific brain regions are associated with current symptoms of inattention and hyperactivity. These associations differ based on whether anxiety and depression symptoms are also present. The brain regions identified in the study

are sensitive to age-related decline, which highlight a need for a greater understanding of inattention and hyperactivity symptoms in later life. Acknowledgments The authors are grateful to Anthony Jorm, Bryan Rodgers, Helen Christensen, Chantal Reglade-Meslin, Patricia Jacomb, Karen Maxwell, Andrew Janke, Perminder Sachdev, and the PATH interviewers. The authors would like to thank Richard Burns for help with SEM analyses.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by the National Health and Medical Research Council (NHMRC) of Australia Program Grant 179805 and Project Grants 1002160, 973302 and 418039 and the Canberra Hospital Salaried Doctors Private Practice Trust Fund. DD is funded by NHMRC Project Grant No. 1043256. NC is funded by ARC Future Fellowship No. 120100227. KJA is funded by NHMRC Research Fellowship No. 1002560. This research was partly undertaken on the NCI National Facility in Canberra, Australia, which is supported by the Australian Commonwealth Government.

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Author Biographies Debjani Das, PhD, works as a postdoctoral researcher with Simon Easteal at the John Curtin School of Medical Research at the Australian National University. She has a background in molecular-genetics and her research interests include investigation of genetic variation in the dopamine pathway genes, attention deficit hyperactivity disorder in adults, and identification of genetic and environmental risk and protective factors for cognitive aging. Nicolas Cherbuin, PhD, is a psychologist and ARC research fellow who leads the Neuroimaging and Brain Lab at the Centre for Research on Ageing, Health, and Wellbeing at the Australian National University. His main research interests are the investigation of risk factors for dementia and cognitive aging, early identification of individuals at risk of developing dementia, and the investigation of neuroanatomical changes associated with cognitive decline and dementia using brain imaging techniques. Kaarin J. Anstey, PhD, FASSA, is Director of the Centre for Research on Ageing, Health, and Wellbeing at the Australian National University and the Principal Investigator of the PATH Through Life Project that focuses on the development of common mental disorders and cognitive function over the adult life course. Her research focuses on risk and protective factors for cognitive aging, longitudinal studies, healthy aging, and older drivers. Walter Abhayaratna, MBBS, is a senior staff specialist, consultant cardiologist, and Clinical Director of Medicine, Canberra Hospital and Health Services. His PhD at the Australian National University was in the field of cardiovascular epidemiology and echocardiography, and he completed a 3-year fellowship in the Echocardiography Laboratory at Mayo Clinic, Rochester, USA. He holds a number of NHMRC research grants for clinical research into the prevention of cardiovascular disease and is the Director of Clinical Trials at ACT Health. He is the professor of Cardiovascular Medicine at the Australian National University. Simon Easteal, PhD, MBA, is Head of the Genome Diversity and Health Group in the John Curtin School of Medical Research at the Australian National University. His research focuses on the evolution of human diversity and the influence of our evolutionary past on current heath and well-being. He has served on numerous editorial boards, advisory committees, government taskforces, working parties, and other bodies in public and private organizations, and he has provided expert opinion in relation to genetic evidence used in court cases in most Australian jurisdictions.

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