Neural correlates of the ADHD self-report scale

Journal of Affective Disorders 263 (2020) 141–146

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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Research paper

Neural correlates of the ADHD self-report scale a,#,1

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Abigail A. Testo , Julia M. Felicione , Kristen K. Ellard , Amy T. Peters , Tina Chou , Aishwarya Gosaia, Emily Hahna, Conor Sheab, Louisa Sylviaa, Andrew A. Nierenberga, ⁎ Darin D. Doughertya, Thilo Deckersbacha, a b

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Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States Department of Medicine, Boston University School of Medicine, Boston, MA, United States

ABSTRACT

Background: The ADHD Self Report Scale is a self-report measure that assesses attentional problems. We sought to validate the ASRS by establishing neural correlates using functional magnetic imaging in healthy controls and individuals with bipolar disorder (BD), who commonly exhibit attentional problems. Methods: ASRS questionnaires and functional MRI data in conjunction with the Multi-source Interference Task (MSIT) were collected from 36 healthy control and 36 BD participants. We investigated task specific changes in the dorsal anterior cingulate cortex (dACC, Brodmann area 32) and their correlations with ASRS subscale scores, inattention and hyperactivity, in both cohorts. Results: As hypothesized, the dACC showed significant increases in BOLD activation between the interference and noninterference conditions. For the ASRS scale as well as its Inattention and Hyperactivity subscales, there was a significant negative correlation with the dACC BOLD for the whole group. Conclusions: The ASRS is sensitive to attentional difficulties in BD, suggesting that it is a valid tool for assessing attentional difficulties in patients with BD.

1. Introduction Bipolar disorder (BD) is characterized by periods of mania, and most often depression (American Psychiatric Association, Diagnostic and statistical manual of mental disorders, fifth edition). Two thirds of patients with BD experience a moderate to severe impact of the illness on occupational and psychosocial functioning, leading it to be the sixth leading cause of disability worldwide (Kogan et al., 2004, Murray et al., 1996). While depressive symptoms appear to be most consistently related to lower overall psychosocial functioning (Bauer, 2001, Goldberg and Burdick, 2008, Huxley and Baldessarini, 2007, Merikangas et al., 2007, Kogan et al., 2004), the role of cognitive impairments is increasingly highlighted. Patients with BD have cognitive difficulties in the area of attention, memory, and executive functioning (for an indepth review, see Goldberg and Burdick, 2008) such as difficulties focusing, increased distractibility, and difficulties organizing complex tasks (Deckersbach et al., 2010; Martinez-Aran et al., 2004; MartinezAran et al., 2007). These impairments are associated with lower occupational functioning and increased rates of disability in BD (AtreVaidya et al., 1998; Martinez-Aran et al., 2004; Martinez-Aran et al., 2007; Deckersbach et al., 2010; Dittman et al., 2007; Gildengers et al., 2007; Altshuler et al., 2008; Dickerson et al., 2004;Brissos et al., 2008). Various self-report measures have been developed to assess

attentional problems, one of which is the ADHD Self Report Scale (ASRS,Adler et al., 2006). The ASRS is a checklist of inattention and hyperactivity symptoms, yielding both a global score and subscores for inattention and hyperactivity. It contains 18 items to assess the 18 symptoms specified in the DSM-IV-Text Revision and is based on earlier clinician and parental report scales for ADHD, such as the ADHD Rating Scale (Adler and Cohen, 2004, Adler et al., 2005, Kessler et al., 2005). The ASRS has demonstrated good sensitivity, specificity, and test-retest reliability in clinical validation studies (Kessler et al., 2007, Matza 2010, van de Glind 2014,Adler et al., 2006, Wyrwich et al., 2015) and has been recommended for use in epidemiological studies (Kessler et al., 2007). It has also been used as a correlate in multiple neuroimaging studies of ADHD (Stark 2010, Wolf et al., 2009, Plichta et al., 2009,Kucyi et al., 2015). These neuroimaging studies found that ASRS scores were correlated with decreased activation in attention processing regions and regions involved in working memory such as vlPFC, superior colliculus, cerebellar default-mode network. To date however, no studies have utilized the ASRS to examine attentional dysfunction in patients diagnosed with BD and their neural correlates. Behavioral and neuroimaging studies have shown that individuals with ADHD tend to perform more poorly on tasks designed to measure attentional functioning, such as the Multi-Source Interference Task (MSIT). The MSIT is a stroop-like cognitive interference task requiring

Correspondence to: Division of Neurotherapeutics, Massachusetts General Hospital, Bldg. 149 13th Street, Office #2628, Charlestown, MA 02130, United States. E-mail address: [email protected] (T. Deckersbach). # Co indicates Co-first authorship. 1 Co-first Authors: Abigail Testo, Julia Felicione. ⁎

https://doi.org/10.1016/j.jad.2019.10.009 Received 15 March 2019; Received in revised form 21 September 2019; Accepted 8 October 2019 Available online 01 November 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

Journal of Affective Disorders 263 (2020) 141–146

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participants to ignore distractor stimuli that will impede the processing of the target stimuli and potentially affect task performance. During imaging, MSIT tasks have been shown to robustly and reliably activate the dorsal anterior cingulate (Bush et al., 2003, 2006) in healthy individuals. When performing the MSIT like tasks, patients with attention problems have been characterized by hypoactivation of the dACC and compared to controls during functional magnetic resonance imaging and other regions implicated in attentional processing (fMRI; Bush et al., 2003, Bush, 2010, Gruber et al., 2017, Ellard et al., 2017, Zilverstand et al., 2017). Patients with BD also show impaired performance on the MSIT and hypoactivation of attentional circuits (Gruber et al., 2017). The purpose of this study was to validate the ASRS for assessing attentional difficulties in BD compared to healthy control participants. We examined its neural correlates using an MSIT paradigm in conjunction with fMRI. Based on the nature of the cognitive impairments seen in BD, we hypothesized that BP group will have higher ASRS scores than the control group, most notably with the inattentive subscale. We also hypothesized that, like with ADHD, attentional dysfunction as measured by the ASRS will be correlated with dACC activation such that more severe ADHD symptoms will be associated with decreased dACC activation during the MSIT.

female). Diagnosis and status of healthy control participants were confirmed. Assessment of all participants was confirmed by clinician rating using the MINI, Mini-International Neuropsychiatric Interview (Sheehan 1998). All participants provided written informed consent prior to participation in accordance with the guidelines of the Internal Review Board of the Massachusetts General Hospital. All participants were medically healthy by self-report and had no history of head injury, seizure, neurological condition, or current major medical conditions. 2.2. fMRI paradigm 2.2.1. MSIT Task All participants completed an affective version of the Multi-Source Interference Task (MSIT) while undergoing fMRI. The MSIT paradigm was set up in a rapid-event related design. Trial stimuli was presented on the screen for 1.7 s, followed by a intertrial interval (ITI) fixation cross of varying lengths. The trial and ITI sequence was determined using Optseq2 (http://surfer.nmr.mgh.harvard.edu/optseq, Dale 1999). During each trial of the MSIT, a three-digit number (comprised using the numbers 0, 1, 2, or 3) was presented for 1.7 s on the screen. Each set contains two identical distractor numbers and a target number that differed from the distractors. Participants report via a button press the identity of the target number that differs from the two distractor numbers. Traditionally, the MSIT task consists of two conditions. During the noninterference (control) condition, distractor numbers are always zeros and the identity of the target number always corresponds to its position on the button response pad (100, 020, 003). However, during the interference condition, distractor numbers are always numbers other than 0 and the identity of the target number is always incongruent with its position on the button response pad (e.g., 211, 232, 331, etc.). In this version of the MSIT, trials were overlaid on positively, negatively, or neutrally valenced pictures of the International Affective Picture System (IAPS) (Lang et al., 1997) this was done as part of an analysis for Ellard et al. (2017), which was the primary paper this data set was collected for. For this study we investigated the interference effect and therefore collapsed over positive, negative, and neutral IAPs conditions for analysis.

2. Methods 2.1. Participants Study participants consisted of 36 individuals with DSM-IV bipolar I disorder (16 female; for demographics see Table 1) recruited through the Dauten Family Center for Bipolar Treatment Innovation at the Massachusetts General Hospital and 36 healthy control participants (22 Table 1 Demographics of bipolar and healthy control participants. Demographic information and results on the Multi Source Interference Task (MSIT) and ADHD Self Report Scale (ASRS) of Bipolar Disorder Patients (BPD) compared to controls showing mean (M) and standard deviation (SD). Demographics

BPD M SD

Controls M SD

Age (years)

25 5.2

35 12.7

Education (years) Measures MSIT reaction time (ms)

16.2 1.5 M SD

15.4 1.9 M SD

Interference trials

860.61 89.42

819.72 99.94

Noninterference trials

650.85 93.98

610.09 95.68

Interference-noninterference

209.757 40.12

209.63 43.93

Overall mean

755.7 89.51

714.91 95.34

Interference trials

15.94 13.55

9.77 8.60

Noninterference trials

2.34 4.77

1.39 3.86

Interference-noninterference

13.60 11.56

8.38 6.17

2.4. MRI data analysis

Overall mean

9.14 8.36

5.58 5.76

Global score

32.97 13.94

19.56 9.79

Inattention score

11.28 5.19

6.89 3.88

Hyperactivity score

21.69 10.05

12.67 6.27

Functional Data were processed using SPM8 software (Wellcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac. uk/spm). Each individual's FMRI images were slice time corrected using slice 7 as a reference, motion corrected using 2nd degree B-spline interpolation, coregistered to the T1 MPRAGE sequence and segmented into white, grey and CSF, spatially normalized to the standardized space established by the Montreal Neurological Institute (MNI; www.bic.mni. mcgill.ca), resampled to 2mm3 voxels (anatomical images were

2.3. MRI scanning MRI data sets were acquired using a 3.0-T whole-body scanner (Trio-System), equipped for echo planar imaging (Siemens Medical Systems, Iselin NJ) with a 3-axis gradient head coil. Head movements were restricted using foam cushions. Images were projected using a rear projection system and E-Prime stimulus presentation software was used to show the task stimuli (Psychology Software Tools; http://www. psychology-software-tools.mybigcommerce.com). Following automated scout and shimming procedures, two high-resolution 3D MPRAGE sequences (TR = 2530 ms, TE = 3.39 ms, flip angle=7°, voxel size = 1.3 × 1.0 × 1.3 mm) were collected for positioning of subsequent scans. fMRI images (i.e., blood oxygenation level dependent signal or BOLD) were acquired using T2* -weighted sequence (27 axial slices aligned perpendicular to the plane intersecting the anterior and posterior commissures, 5 mm thickness, skip 1 mm, TE = 30 ms, TR = 1700 ms, flip angle = 90°).

MSITerror rate(%)

ASRS scores

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resampled to 1mm3 voxels), and smoothed with a three-dimensional Gaussian kernel of 6 mm full-width half maximum(FWHM). All collected data had minimal head motion (< 3 mm linear movement in the orthogonal planes; <0.5° radians of angular movement) and motion artifacts were comparable between groups. The general linear model was applied to the time series, convolved with the canonical hemodynamic response (Friston et al., 1995) function and a 128 s high-pass filter. Serial autocorrelations were addressed with an AR(1) model. Movement parameters, derived from motion correction in the preprocessing, were included in the model as regressors of no interest. For each subject, condition effects were modeled with the SPM canonical hemodynamic response function. For each subject (first-level analysis, individual subjects’ analysis), condition effects were estimated at each voxel and contrast images were produced for the contrast interferencecontrol (i.e., interference–non-interference.). The contrast image for the Interference–Non-Interference contrast from both the BD participants and the healthy controls were entered into a second-level one sample ttest. The a priori specified region of interest (ROI) was the anterior cingulate defined as bilateral Brodmann area 32 using the mask provided by Anatomical Automatic Labeling (Tzourio-Mazoyer et al., 2002) tool implemented in the WFU Pickatlas http://www.ansir.wfubmc.edu) (Maldjian et al., 2004; Maldjian et al., 2003, 2004). For the a priori specified region of interest, a statistical threshold of p < 0.05 was adopted and a cluster threshold of 129 voxels was applied to the voxels within our ROI, as determined by a cluster based Alpha Sim correction for multiple comparisons at a p < 0.01 level. Each individuals’ beta estimation from the one sample t-test was extracted using MarsBar software. Those beta values were correlated with each participant's global ASRS score, in addition to the ASRS subscores of inattention and hyperactivity, using Pearson's r. To investigate the effects of ASRS subscores for inattention and hyperactivity on the participants with BPD, a further multiple regression analysis was performed on the beta values from participants with BPD only, using inattention and hyperactivity sub-scores of the ASRS as regressors of interest, covarying for age, gender, education level, medication load (described below) and any changes in medication that occurred in the 4 weeks leading up to the study. Medication load (the effects of psychotropic medications on cognitive functioning) was assessed using an established approach by Phillips et al., 2008, Almeida et al., 2009), Goldberg and Burdick (2008) and Hassel et al., 2008.). For each participant, the dose of each class of medication (e.g., antidepressant, mood-stabilizer, antipsychotic, and anxiolytic) is coded as absent (0), low (1), or high (2) using the dosing guidelines and a participant's total medication load is reflected as a sum of these scores.

3.2. Behavioral data Reaction Times: There was a significant increase in reaction time in the interference condition compared to the non-interference condition (F = 168.53, df = 1138, p < 0.001, see Table 1). Bipolar participants showed significantly slower reaction times across both the interference and non-interference condition (main effect group; F = 6.39, df = 1138, p = 0.01). There was no significant group by condition interaction (F = 0.15, df = 1138, p = 0.84; see Table 1). Error Rates: Error Rates were higher in the interference compared to the non-interference condition (F = 56.33, df = 1138, p < 0.001). BD subjects had higher error rates than HC (F = 5.95, df = 1138, p = 0.02) across the interference and noninterference condition (Main effect group). The group by condition interaction did not reach significance (F = 3.2, df = 1138, p = 0.08). Participants with BD showed higher error rates in the interference condition compared to controls (t = 2.26, df = 35, p = 0.026), but not in the non-interference condition (t = 0.91, df = 35, p = 0.37). 3.3. fMRI results Main Effect of Task: Interference –NonInterference: As hypothesized, the one-sample t-test revealed significant increases in dACC activation between the interference and noninterference conditions (BA 32; max voxel: x = 6, y = 6, z = 52, KE = 303, P < 0.001). The mean difference in dACC activation between BD and HC in this cluster was trending, but did not reach significance (t = 1.8, df = 70, p = 0.075). BOLD activation in the dACC cluster for the whole group (BD and HC) was significantly correlated with global ASRS scores (r = -0.37, p = 0.001, max voxel: x = 20, y = 8, z = 48, Fig. 1). This effect was largely driven by the BD group (BD: r = -0.35, p = 0.04, HC: r = -0.29, p = 0.08). For the inattention scale, there was a significant correlation with the dACC BOLD for the whole group (r = -0.32, df = 70, p = 0.01; BD: r = -0.29, 0 = 0.09; HC: r = -0.28, p = 0.10). For the hyperactivity scale, there were significant correlations with BOLD values for the whole group (r = -0.29, df = 70, p = 0.01; BD: r = -0.39, p = 0.02 and HC: r = -0.28, p = 0.10). Differences in RT and percent error between the Interference and Noninterference conditions during the MSIT task were not correlated with dACC activation (r = -0.09, p = 0.46). 4. Discussion We sought to validate the ASRS (ADHD Self Report Scale) for assessing attentional difficulties by examining the relationship between the ASRS and dACC activation, a region implicated in attentional processing, using an MSIT paradigm in conjunction with fMRI. For instruments such as the ASRS to be valid, they rely on a respondent's ability to accurately self-report on the included items. Self-report measures are subject to various reporting biases that may affect the answer to the items; including social desirability biases and memory biases (Grimm, 2010, Coles and Heimberg, 2002) and these biases may affect the quality of the self-report data. Therefore objective behavioral and/or neural data is desirable to validate self-report instruments such as the ASRS. Based on the fMRI correlates found in both healthy control participants as well as participants with bipolar disorder in the present study, our results lend support to the validity of the ASRS as a selfreport instrument for attentional difficulties. Specifically, our study replicates previous fMRI findings, showing increased dACC activation during the Interference-Noninterference condition in the MSIT among all participants (Bush et al., 2003, 2006). Decreased dACC activation was found between BD and HC that was trending towards statistical significance in our peak cluster (Bush et al., 2003, Bush, 2010, Zilverstand et al., 2017). ASRS scores both in the full scale as well as in the inattention and hyperactivity subscales were negatively correlated with activation in the dACC in the overall group. BD participants in the present study compared to healthy controls did not show

3. Results 3.1. Participants Demographic and clinical information for participants can be found in Table 1. ASRS Scale: ASRS total scores (and corresponding subscales for Inattention and Hyperactivity) were higher in the BD group than the HC group (t = 4.48, df = 70, p < 0.001). With respect to the ASRS subscales, participants with BD exhibited higher scores in the ASRS Inattention scale (t = 4.84, df = 70, p < 0.001) and the ASRS Hyperactivity scale compared to controls (t = 5.06, df = 70, p < 0.001). ASRS scores were not correlated with changes in reaction times or with changes in error rates between the Interference and Noninterference condition (Behavioral data presented below) for either BD or HC (all p’s > or = 0.5).

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Fig. 1. dACC (Brodmann area 32) activation in the contrast Interference - Noninterference for both BPD and healthy controls, threshold at p < 0.05 uncorrected to see the full extent of activation. Legend shows increases in T scores. MNI-Coordinates x = 20, y = 8, z = 48. Max voxel Z-score 3.20. The scatterplot shows the inverse relationship between ASRS global score and change in BOLD activation in the peak cluster within the dACC ((x = 20, y = 8, z = 48) ke = 210) in the contrast Interference–Noninterference for BPD and healthy controls (r = -0.37, p = 0.001).

disproportionate slowing of reaction times or error rates in the interference vs. non-interference condition. This is inconsistent with studies that have shown neuropsychological impairments in individuals with bipolar disorder even when they are euthymic (neither depressed or manic). Overall, these performance decrements (on the group level) typically range between 0.5 and 1.5 standard deviations below that of healthy control cohorts (Deckersbach et al., 2005, 2006, Goldberg and Burdick, 2008, Atre-Vaidya et al., 1998, Martinez-Aran et al., 2004, Martinez-Aran et al., 2007, Deckersbach et al., 2010, Dittman 2007, Gildengers et al., 2007, Altshuler et al., 2008, Dickerson et al., 2004,Brissos et al., 2008). From this perspective, it is noteworthy that the bipolar cohort in this study was not specifically selected for this study based on neuropsychological impairments and that not all individuals with bipolar disorder exhibit cognitive impairments. It is also worth discussing other methodological limitations of the current study. It is acknowledged that this argument would be strengthened by comparing results using the ASRS with a different clinical instrument already validated to assess cognitive problems in patients with bipolar disorder, however, there was no other validated clinical instrument for attentional difficulties used in this study. Another limitation of the study is that fMRI is not a direct measure of brain activity. Finally, it should be noted that other studies in bipolar disorder have found hyperactivation in the dACC using cognitive/affective probes

(Deckersbach et al., 2009). This inconsistency suggests that there is no generalized impairment in activating dACC. Rather, it's functional impairment is revealed in the special context it is probed in. This may include mood state and the type of fMRI task that is being used. Despite these inconsistencies, the overall findings provide some additional support that the ASRS is a valid self-report instrument for assessing attentional difficulties in individuals with bipolar disorder. Funding This study was partially funded by NIMH K23MH074895 to Thilo Deckersbach and by the Dauten Family Center for Bipolar Treatment Innovation. This work was supported by a NIHM K23-Career Development Award to Thilo Deckersbach (5K23MH074895) and was also supported in part by the Dauten Family Center for Bipolar Treatment Innovation. Contributors We acknowledge the contribution of both the patients and healthy controls who agreed to participate in our study. 144

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Consultant

Declaration of Competing Interest

Abbott Laboratories, Alkermes, American Psychiatric Association, Appliance Computing Inc. (Mindsite), Basliea, Brain Cells, Inc., Brandeis University, Bristol Myers Squibb, Celexio, Clintara, Corcept, Dey Pharmaceuticals, Dainippon Sumitomo (now Sunovion), Eli Lilly and Company, EpiQ, L.P./Mylan Inc., Forest, Genaissance, Genentech, GlaxoSmithKline, Healthcare Global Village, Hoffman LaRoche, Infomedic, Intra-Cellular Therapies, Lundbeck, Janssen Pharmaceutica, Jazz Pharmaceuticals, Medavante, Merck, Methylation Sciences, NeuroRx, Naurex, Novartis, Neurocrine, Neuronetics, PamLabs, Parexel, Pfizer, PGx Health, Otsuka, Ridge Diagnostics Shire, Sage Pharmaceuticals, Schering-Plough, Somerset, Sunovion, Supernus, Takeda Pharmaceuticals, Targacept, and Teva; consulted through the MGH Clinical Trials Network and Institute (CTNI) for Astra Zeneca, Brain Cells, Inc, Dianippon Sumitomo/Sepracor, Johnson and Johnson, Labopharm, Merck, Methylation Science, Novartis, PGx Health, Schering-Plough, Targacept and Takeda/Lundbeck Pharmaceuticals, NeuroRx Pharma, Pfizer, Physician's Postgraduate Press, Inc., and Assurex.

Abigail Testo has no conflicts of interest. Julia Felicione has no conflicts of interest. Dr. Amy Peters has no conflicts of interest. Dr. Amy Peters's sources of funding include NIH National Institute of Mental Health, Translational Neuroscience Training for Clinicians T32 MH 112485. Dr. Kristen Ellard does not have any conflicts of interest. Dr. Kristen Ellard's sources of funding include NIH National Institute of Neurological Disorders and Stroke (NINDS) Training Program in Recovery and Restoration of CNS Health and Function (T32 NS10066301). Aishwarya Gosai has no conflicts of interest. Emily Hahn has no conflicts of interest. Conor Shea has no conflicts of interest. Dr. Louisa Sylvia reports grants from the Elizabeth Dole Foundation, National Institute of Health, American Foundation of Suicide Prevention, Takeda, and PCORI as well as personal fees from Clinical Trials Network and Institute and New Harbinger. Dr. Darin Dougherty has no conflicts of interest. Dr. Deckersbach's research has been funded by NIH, NIMH, PCORI, NARSAD, TSA, IOCDF, Tufts University, DBDAT, Otsuka Pharmaceuticals, Cogito, Inc, Sunovion. and Assurex. He has received honoraria, consultation fees and/or royalties from the MGH Psychiatry Academy, BrainCells Inc., Clintara, LLC., Systems Research and Applications Corporation, Boston University, the Catalan Agency for Health Technology Assessment and Research, the National Association of Social Workers Massachusetts, the Massachusetts Medical Society, Tufts University, NIDA, NIMH, Oxford University Press, and Guilford Press. He has also participated in research funded by Assurex, DARPA, NIH, NIMH, NIA, AHRQ, PCORI, Janssen Pharmaceuticals, The Forest Research Institute, Shire Development Inc., Medtronic, Cyberonics, Northstar, Takeda, and Sunovion.

Grants/research support American Foundation for Suicide Prevention, AHRQ, Brain and Behavior Research Foundation, Bristol-Myers Squibb, Cederroth, Cephalon, Cyberonics, Elan, Eli Lilly & Company, Forest, GlaxoSmithKline, Intra-Cellular Therapies, Janssen Pharmaceuticals, Lichtwer Pharma, Marriott Foundation, Mylan, NIMH, PamLabs, Patient Centered Outcomes Research Institute (PCORI), Pfizer Pharmaceuticals, Shire, Stanley Foundation, Takeda/Lundbeck, and Wyeth-Ayerst.

Acknowledgments None.

Honoraria

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