Scholarly article on topic 'Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study'

Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study Academic research paper on "Clinical medicine"

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Academic research paper on topic "Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study"

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Neuropsychiatry Imaging and Stimulation

Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study

T. Sigi Hale, Andrea M Kane, Olivia Kaminsky, Kelly L Tung, Josh F Wiley, James J McGough, Sandra K Loo and Jonas T Kaplan

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Frontiers in Psychiatry 1664-0640

Original Research Article 03 Apr 2014 26 Jun 2014 26 Jun 2014 www.frontiersin.org

Hale T, Kane AM, Kaminsky O, Tung KL, Wiley JF, Mcgough JJ, Loo SK and Kaplan JT(2014) Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study. Front. Psychiatry 5:81. doi:10.3389/fpsyt.2014.00081

© 2014 Hale, Kane, Kaminsky, Tung, Wiley, Mcgough, Loo and Kaplan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Counts Abstract: 258 Main Body: 8,765 Figures: 7

Visual Network Asymmetry and Default Mode Network Function in

ADHD: An fMRI Study1

T. Sigi Halea, Andrea M. Kanea, Olivia Kaminskya, Kelly L. Tunga, Josh F. Wileyc, James, J.

McGougha, Sandra K. Looa, Jonas T. Kaplanb

department of Psychiatry and Biobehavioral Sciences, UCLA Semel Institute for Neuroscience and Human Behavior

bBrain and Creativity Institute and Department of Psychology, University of Southern California 3520A McClintock Ave Suite 251, Los Angeles, CA 90089-1061

cDepartment of Psychology, UCLA

Correspondence and Reprint Requests: Address:

T. Sigi Hale.

UCLA Semel Institute

760 Westwood Plaza, Room 48-228B

Los Angeles, CA 90024

Phone: 310-825-8660

FAX: 310-206-4446

e-mail: sig@ucla.edu

Short title: Asymmetry, ADHD, and The Default Mode Network

1 This work was funded by National Institute of Mental Health Grant by the National Institute of Mental Health Grant MH082104 (PI Hale).

Abstract

Background: A growing body of research has identified abnormal visual information processing in ADHD. In particular, slow processing speed and increased reliance on visuo-perceptual strategies have become evident. Objective: The current study used recently developed fMRI methods to replicate and further examine abnormal rightward biased visual information processing in ADHD and to further characterize the nature of this effect; we tested its association to several large-scale distributed network systems. Method: We examined fMRI BOLD response during letter and location judgment tasks, and directly assessed visual network asymmetry and its association to large-scale networks using both a voxelwise and an averaged signal approach. Results: Initial within-group analyses revealed a pattern of left lateralized visual cortical activity in controls but right lateralized visual cortical activity in ADHD children. Direct analyses of visual network asymmetry confirmed atypical rightward bias in ADHD children compared to controls. This ADHD characteristic was atypically associated with reduced activation across several extra-visual networks, including the default mode network (DMN). We also found atypical associations between DMN activation and ADHD subjects' inattentive symptoms and task performance. Conclusion: The current study demonstrated rightward VNA in ADHD during a simple letter discrimination task. This result adds an important novel consideration to the growing literature identifying abnormal visual processing in ADHD. We postulate that this characteristic reflects greater perceptual engagement of task-extraneous content, and that it may be a basic feature of less efficient top-down task-directed control over visual processing. We additionally argue that abnormal DMN function may contribute to this characteristic.

Key words: attention, laterality, asymmetry, sensory, verbal, default, spatial, network

1. Introduction

Abundant research has identified abnormal frontal-striatal brain function in attention-deficit hyperactivity disorder (ADHD) (Durston et al., 2011). However, a growing body of work now also implicates abnormal posterior brain functions, and associated abnormalities of early-stage sensory information processing (Cortese et al., 2012). These domains have not yet been conceptually integrated, and we suspect this partly underlies why the more recent findings implicating abnormal sensory processing have been slow to gain widespread interest. The current study seeks to address this issue. First, we examine a specific aspect of low-level information processing in ADHD that our and others' work have identified to be abnormal. Next, we explore how this characteristic relates to several large-scale distributed network systems, many of which are implicated in ADHD (Castellanos and Proal, 2012;Cortese et al., 2012). Our goal is to help further substantiate and characterize abnormal information processing in ADHD, and to examine whether and how this characteristic relates to network-level brain functions.

Multiple functional imaging studies have shown abnormal activation or metabolic effects during rest and/or sub-executive operations in ADHD (Zametkin et al., 1990;Zametkin et al., 1993;Seig et al., 1995;Chabot and Serfontein, 1996;Ernst et al., 1998;Baving et al., 1999;Swartwood et al., 2003;Hale et al., 2007;Hale et al., 2009b), which clearly indicates that ADHD abnormal brain function is not limited to higher-order operations. More direct support for low-level sensory information processing deficits comes from several sources. A recent meta-analysis of fMRI studies examining task-based cognition in ADHD identified visual cortical abnormalities to be a key finding in ADHD (Cortese et al., 2012). Abnormal visual cortical structure has also been identified (Wang et al., 2007). Event related potential (ERP) studies also directly implicate early sensory processing abnormalities in ADHD (i.e., abnormal N1, N2, P2, P3) (for review: Johnstone et al., 2013), while neurocognitive studies provide additional strong evidence for both perceptual processing (Weiler et al., 2002;Lenz et al., 2008;Nazari et al., 2010;Stevens et al., 2012), and naming speed deficits (Semrud-Clikeman et al., 2000;Tannock et al., 2000;Weiler et al., 2000;Nigg et al., 2002;Rucklidge and Tannock, 2002;Stevens et al., 2002;Brock and Christo, 2003;Willcutt et al., 2005).

Our research in this domain begins with the precept that complex task-directed actions are likely to rely on a specific manner of sensory information processing that facilitates fast categorical parsing of sensory data. To illustrate, if a person wants to find a pen on a cluttered countertop in order to sign a document, it is task-adaptive to quickly identify (i.e., categorize) that stimulus using the minimal sensory detail required. Here, the pen's aesthetic details and any surrounding content are task-extraneous. Alternatively, if an artist wants to paint a still-life portrait of this pen, they should indulge as much detail as possible. One approach seeks to identify a stimulus using the minimal sensory detail required. The other seeks to indulge as much sensory detail as possible in order to produce a prolonged sensory immersive experience. We theorize that ADHD involves a reduced capacity for the former mode of processing.

This task-specialized manner of sensory information processing likely depends on the coordinated function of multiple distributed brains systems that get dynamically integrated in service to task-directed actions (For model description: Hale, 2014). In this view, any impairment to this system, no matter the cause, should result in less efficient task-directed top-down control over sensory information processing, with an associated increased exposure to task-extraneous content. In other words, poor task-directed sensory information processing should result in a greater proportion of off-task visual sensory details being processed. This bias towards sensory immersion/detail over categorical processing may be indexed by an increased contribution of right-lateralized visuo-perceptual

processing.

Evidence from our previous behavioral laterality studies in ADHD adults supports the presence of a right-hemisphere bias. These demonstrated greater RH contribution to processing task stimuli, associated left hemisphere (LH) linguistic impairments, and abnormal interhemispheric interaction (Hale et al., 2005;Hale et al., 2006;Hale et al., 2009a). This work also showed that this pattern was reflective of an abnormal brain-state orientation (rather than capacity) (Hale et al., 2006), bore advantages for RH specialized abilities (Hale et al., 2006), and impacted high-order cognition (Hale et al., 2009a). Using fMRI and EEG we further uncovered that RH bias in ADHD was only evident during sub-executive operations (Hale et al., 2007), exhibited stronger expression with greater ADHD family loading (Hale et al., 2010a), and stronger expression among carriers of the DRD4-7 repeat allele and other ADHD risk-factors. Finally, a robust and literature-consistent (Clarke et al., 2002;Clarke et al., 2008) biomarker was identified. ADHD subjects exhibit pronounced rightward EEG beta (16-21 Hz) asymmetry in inferior parietal brain regions during the Conner's Continuous Performance Test (CPT) (Hale et al., 2010b), a finding we he have since replicated (unpublished).

Although not yet widely understood, this pattern of findings is well aligned with extant ADHD literature. As noted, slow naming speed is identified in ADHD, which is consistent with impoverished LH contribution to sensory encoding. Previous behavioral laterality studies of ADHD have also indicated increased RH contribution (Malone et al., 1988;Campbell et al., 1996). Functional imaging studies at rest or during simple (i.e., sub-executive) challenges have shown a pattern of reduced LH (Zametkin et al., 1990;Zametkin et al., 1993;Seig et al., 1995;Ernst et al., 1998), and/or increased RH contribution (Chabot and Serfontein, 1996;Baving et al., 1999;Swartwood et al., 2003;Hale et al., 2007;Hale et al., 2009b), and recent diffusion tensor imaging studies have reported greater RH parietal (Silk et al., 2009) and frontal (Li et al., 2010) fractional anisotropy in ADHD. Furthermore, a lack of normally occurring L>R asymmetry in prefrontal cortical convolution complexity has been reported (Li et al., 2007), as well as increased RH visual cortex volumes (Wang et al., 2007). Finally, identified abnormal posterior corpus callosum size (Seidman et al., 2005) and function (Chabot and Serfontein, 1996;Barry et al., 2005;Clarke et al., 2007;Rolfe et al., 2007), including atypically reversed posterior callosal transfer speeds (Rolfe et al., 2007), clearly implicate abnormal integration of verbal and perceptual sensory encoding functions.

A similar pattern of reduced LH and increased RH contributions is evident during more complex tasks; however, this literature is more variable, showing diffuse effects consistent with multiple weaknesses across distributed brain-systems (Tamm et al., 2006;Banich et al., 2009;Rubia et al., 2010;Cortese et al., 2012;Cubillo et al., 2012). Still, several studies have shown greater association between ADHD subjects' behavioral performance and right-sided brain structure and function (Casey et al., 1997;Matero et al., 1997;Ernst et al., 2003;Hill et al., 2003;Yeo et al., 2003;Vaidya et al., 2005;Casey et al., 2007;Burgess et al., 2010), and EEG studies that have directly examined activation asymmetries and/or that directly compared left-right differences have consistently shown R>L patterns in posterior brain regions (Chabot and Serfontein, 1996;Baving et al., 1999;Clarke et al., 2002;Clarke et al., 2008;Hale et al., 2009b;Hale et al., 2010a;Hale et al., 2010b). Finally, a recent meta-analysis of ADHD functional imaging has reported hyper-activation of the strongly right-lateralized ventral attention network (VAN), noting it may be related to increased distractibility in this population (Cortese et al., 2012), which is consistent with reports showing that greater activation in this network is associated with attentional shifting and/or bottom-up visuo-perceptual processing (Downar et al., 2001 ;Zeithamova and Maddox, 2007;Bubic et al., 2011;Roser et al., 2011).

Thus, the literature strongly implicates some form of increased weighting of non-verbal sensory processing in ADHD. We hypothesize that this stems from variable impairments to task-directed brain functions that otherwise facilitate fast/efficient identification and verbal encoding of task relevant stimuli (for model description: Hale, 2014). Still, abnormal processing asymmetry has been inconsistently observed during complex EF-level operations. We suspect this is because the operative feature of abnormal sensory information processing in ADHD is the relative, rather than absolute, contribution of left and right hemisphere sensory functions, and few studies are designed to identify such effects.

Methods for the direct analysis of EEG asymmetry are well developed, and as noted, have consistently shown R>L patterns in ADHD. However, related fMRI methods to assess the asymmetry of BOLD signal have only recently begun to overcome methodological difficulties involving thresholding techniques (Wilke and Lidzba, 2007;Abbott et al., 2010). The current study utilizes these novel fMRI methods to further examine and substantiate abnormal information processing asymmetry in ADHD. Our previous studies indicated that ADHD rightward biased processing is maximally evident during linguistic challenges, and that it underlies linguistic impairments (Hale et al., 2005;Hale et al., 2006;Hale et al., 2009a;Hale et al., 2010b). Hence, we utilized an fMRI paradigm that presents word stimuli and requires subjects to make either a letter discrimination or spatial judgment in different blocked conditions. This task has been previously shown to elicit lateralized activations for the letter and spatial judgments (Stephan et al., 2003). Given the exclusive use of word stimuli in this study, we hypothesized that ADHD children would show a general pattern of increased rightward asymmetry in visual cortical regions compared to controls. However, we also hypothesized that this effect would be maximally robust during the letter discrimination condition that requires a more fixed attentional set and fast verbal categorizations.

Furthermore, since we theorize that asymmetry in low-level perceptual processing is directly related to abnormalities in higher level processing, we sought to understand the relationship between perceptual asymmetry and activity in other brain networks. Patterns of intrinsic functional connectivity in the brain have revealed multiple networks of brain regions whose activity is correlated during rest (Yeo et al., 2011). Several of these networks have been implicated in ADHD (Castellanos and Proal, 2012;Cortese et al., 2012). Among these, abnormal default mode network (DMN) function has been the most widely reported (Cortese et al., 2012). Although previously understood as a resting or task-negative network (Fox et al., 2005), the DMN is now also understood to play a role in internally directed self-referential aspects of cognition (Spreng, 2012), including internal aspects of task-directed cognition (Hampson et al., 2006; 2012). In fact, recent work has even suggested a link between DMN integrity and verbal working memory capacity (Yakushev et al., 2013). This raises the intriguing possibility that abnormal DMN function in ADHD might be associated with a reduced capacity to orchestrate the internal aspects of task-directed cognition (e.g., planning, sequencing, maintaining, and updating task directives) (Desimone and Duncan, 1995;Baddeley et al., 2001;Olivers et al., 2011;de Fockert, 2013), possibly undermining a general capacity for task-directed brain functions, including task-specialized sensory information processing (Uddin et al., 2009). To examine this possibility, the current study examines, as a secondary aim, whether visual processing asymmetry in ADHD is uniquely associated with DMN function. To test the specificity of any such effects other networks are also examined.

2. Material and Methods

2.1 Subjects

Subjects were recruited from Los Angeles County and the surrounding regions using a database of participants from previous UCLA studies who indicated they were willing to participate in future studies. Subjects were also recruited through flyers posted near UCLA, and advertisements on focus group websites (e.g., parenting blogs). Given our interest to examine ADHD-specific asymmetry effects, we chose to limit possible variability in brain-laterality due to gender, handedness, and/or variation in pubertal onset (Toga and Thompson, 2003). Accordingly, participation required being male, right-handed, and between the ages of 11 and 17, with an initial parental report that the child had begun using deodorant, with puberty-onset later confirmed by a parent during a private interview.

After receiving verbal and written explanations of study requirements a parent and the participating child provided written informed consent/assent, as approved by the UCLA Institutional Review Board. To screen for ADHD and other psychiatric disorders using DSM-IV criteria, participating children and their mothers were interviewed using the semi-structured interview of the Schedule for Affective Disorder and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL) (Kaufman et al., 1997). Autism was ruled out via the Social Communication Questionnaire (Berument et al., 1999). Diagnostic interviews were conducted by a highly trained clinical interviewer (MA Psychology), after which, 'best estimate' diagnoses were determined from individual review of diagnoses, symptoms, and impairment level by a board certified child psychiatrist (Leckman et al., 1982). Inclusion of ADHD subjects required a current diagnosis of ADHD (6 or more symptoms on inattentive and/or hyperactive subscales). Inclusion of non-ADHD controls required no evidence of past or current ADHD (i.e., reporting 4 or fewer ADHD symptoms on inattentive and hyperactive subscales), and no known cases of ADHD among 1st degree relatives. Subjects were excluded based on the following criteria: past or current documented neurological disorder, a significant head injury resulting in loss of consciousness, a diagnosis of schizophrenia or autism (self or first degree relative), or an estimated Full Scale IQ < 80.

Handedness was assessed with a shortened version of the Edinburgh Handedness Inventory (Oldfield, 1971). This scale uses seven questions regarding hand preference and produces scores ranging from negative 14 (indicating maximum left-handedness), to positive 14 (indicating maximum right-handedness). Assessment of verbal ability was performed to help rule out the possibility of undiagnosed comorbid reading difficulties in ADHD contributing to asymmetry effects. We used age normed scores from the vocabulary subtest of the Wechsler's Intelligence Test for Children 3rd edition (WISC-III) (Wechsler, 1981), the reading and spelling subtests of the Peabody Individual Achievement Test-Revised (PIAT-R) (Dunn and Markwardt, 1970), and the word-attack (phonemic awareness) subtest of the Woodcock Johnson-Revised (WJ-R) (Brown et al., 2000). Subject demographic information is presented in table 1. All subjects were enrolled in age-appropriate educational programs as required by California law. Subjects on stimulant medication were asked to discontinue use for 24 hours prior to their visit.

31 ADHD and 25 typically developing right-handed male children between the ages of 11 to 16 underwent fMRI procedures. 10 ADHD subjects were excluded (5 = motion, 2 = non-compliance, 1 = sleep, 1 = non-tolerant of fMRI environment, 1 = image distortion from permanent retainer). 4 control subjects were excluded (1= medical problem that impacted brain development, 1 = father diagnosed with ADHD, 1= borderline ADHD, 1= non-tolerant of fMRI environment). The final sample consisted of 21 ADHD and 21 control subjects. The ADHD sample was 81% Caucasian, 14% African American, and 5% Hispanic. The control sample was 62% Caucasian, 9.5% African American, 19% Hispanic, and 9.5% Asian.

Table 1. Sample Demographics

Clinical Variables Controls N = 21 ADHD N = 21 Statistic

IQ f = 112.7, std= 18.2 f= 110.6 std=15.6 t= .39, p = .70

Age f= 13.1, std=1.5 f= 13, std=1.6 t = .29, p = .77

SES f= 2.9, std=85 f= 2.2, std=87 t = -2.5, p = = .02

ADHD Type n/a 11C, 10I n/a

Anxiety 0 affected 1 affected (GAD) n/a

Mood 0 affected 0 affected n/a

ODD 0 affected 6 affected fe: p=02

CD 0 affected 1 affected n/a

Handedness Score f = 12.7, std= 2.8 f= 12.1 std=2.7 t = .75, p = .46

Vocabulary x = 12.5, std =3.5 f= 11.9, std =3.6 t= .54 , p = .59

Phonology f = 106.5, std= 12.5 £=103.5, std= 12.4 t = .44 , p = = .44

Reading f = 110, std= 9.6 £=102.4, std= 14.2 t = 1.98, p : = .06

Spelling f = 108.9, std= 14.4 f= 98.3, std= 13.5 t= 2.4, p = .02

IQ: estimated from block-design and vocabulary subtest of WISC-III; SES= Socioeconomic status measured by Hollingshead Scale (1975); ADHD Type: C= combined, I=inattentive; Handedness Score= 14 point scale from Edinburgh Handedness Inventory, with 14 indicating maximum right-handedness (see text for description & reference); Anxiety/ Mood reflect definite diagnosis of at least 1 current anxiety and/or mood disorder as assessed by direct interview using K-SADS-PL; ODD/CD = oppositional defiant disorder and conduct disorder as assessed by direct interview using the K-SADS-PL. fe = Fisher's exact test; see text for description of linguistic measures.

2.2 Task Procedures

The fMRI task was adapted from a previous block-design study that uncovered robust laterality differences for 'letter' versus 'spatial' processing in healthy adults (Stephan et al., 2003). In this, and our current study, subjects viewed successive presentations of four letter words presented in black-font with a red letter in the 2nd or 3rd position and had to decide whether the red letter was on the left or right (location condition), or an 'A' or not (letter condition). Subjects responded via button presses, using the index finger to signal a 'left' or 'yes-A' response, and the middle finger to signal a 'right' or 'not-A' response. During baseline, subjects responded when a word appeared (i.e., no decision). Thus the stimuli in all three conditions (letter, location, and baseline) were identical, and only the task instructions differed. The original study used lateralized presentations, however, the authors did not report brain activations differences based on visual field, and so we used central presentations. We also did this to reduce complexity and difficulty given our use of an impaired child sample. The original study also alternated response hand within subjects. We used right-handed responses to assure response related brain activation was eliminated against baseline, and again to avoid unnecessary complexity while working with a child ADHD sample. The original study also used German words. We used English words.

Stimuli were generated using the MRC Psycholinguistic Database (Fearnley, 1997). They consisted of 192 four-letter concrete nouns assessed for word-frequency (Kucera-Francis and Thorndike-Lorge), concreteness, and imagability, and matched across key stimulus parameters (left vs. right, 'A' vs. 'Not A'). Half of the words contained a target letter 'A', and half did not. For each of these sets, the target occurred an equal number of times in the 2nd or 3rd position (i.e., left or right). Baseline conditions used four additional unique stimuli (FLAP, HAND, MILK, CORD).

Two data collection runs were performed. Each presented eight task-blocks (4 location, 4 letter) interspersed with seven baseline conditions. Within runs, the order of task-blocks was randomized, with pre-block instruction screens indicating which task to perform. Task blocks contained twelve two-second randomly jittered trials (± 250ms)- six with target 'A's, six without, and among these sets, an equal number of targets in the 2nd or 3rd position. The order of trial types was randomized within blocks. During trials, words were presented centrally for 150ms in all capital 48-point black-font (except for the red target letter). A central fixation cross was displayed between stimulus presentations. Baseline conditions contained eight trials and used the same stimulus presentation parameters. The 192 task-stimuli were newly randomized for each subject, with no stimuli repeating across both runs. Stimulus presentation and response collection were controlled using MATLAB (The Mathworks, Inc.) and the Psychophysics Toolbox (Brainard, 1997). See supplement part-1 for graphical portrayal of task parameters.

2.3 General Procedures

fMRI procedures were a component of a broader protocol. On the first day, subjects underwent clinical, cognitive, and EEG assessments. On the second day, they underwent fMRI consenting, safety screening, training, and testing. The mean time difference between day 1 and 2 was 91.5 days for ADHD subjects, and 56.3 days for controls (no statistical group difference). Before fMRI scanning, task training occurred via a standardized computer program implemented using E-prime software (Psychology Software Tools, Inc.). Although the program was designed to operate automatically, research staff read aloud the instructions and prompted subjects to repeat any training module not performed above chance. Task training was performed to reduce the likelihood of capturing brain activation associated with task learning during scanning procedures.

The program first introduced subjects to each of the task conditions. This required active participation as subjects learned about stimuli and associated response mappings for each condition (location, letter, baseline). Each of these modules ended with a practice that provided trial-by-trial and overall performance feedback. Next, the program portrayed the intermixing of blocked-conditions and associated instruction screens that signaled which task to perform. The instruction screens were identical to those used in the scanner. These screens were designed to signal which task to perform next, and provide prompts to help children remember condition-specific response mappings (i.e., instruction screen graphics displayed associated response mappings). This task-mixing practice section also ended with a brief practice that provided overall performance feedback. Lastly, subjects underwent a mock run of the experiment exactly as presented in the scanner, barring a few differences (different word stimuli, keyboard responses, overall performance feedback).

Task training took approximately thirty minutes, after which, subjects and a parent walked to the fMRI facility, where they waited in a lounge during set-up. During this time, subjects were encouraged to explore a nearby mock-scanner, listen to recordings of MRI and fMRI scanner noises, and practice inserting earplugs. After fMRI equipment and software set-up was complete, subjects entered the scanner control room and were given an opportunity to become familiar/comfortable with the environment, as well as select a movie to watch during set-up and structural imaging. Upon entering the scanner-room subjects were instructed in the use of critical equipment (response box, head phones, goggles, emergency button) and were allowed to watch their selected movie (via fMRI goggles) during additional set-up and shimming procedures. Throughout these and subsequent scanning procedures, a concerted effort was made to keep children actively engaged and comfortable.

Before running the fMRI task, children were shown a 'start screen'. This reminded them what each of the instruction screens looked like and repeated general task-instructions. A research staff read the

instructions to the subject and prompted them to demonstrate button presses associated with each condition. This assured us that children were using the button box correctly, and it made the subjects aware that we were able to monitor their button-presses in real-time.

2.4 Data Acquisition

This study was conducted at the Staglin IMHRO Center at UCLA. MRI recording was performed with a standard 12-channel head coil on a Siemens 3T Trio Magnetic Resonance Imaging System with TIM. Two functional runs including 195 volumes each were acquired. These images were collected over 33 axial slices covering the whole cerebral volume using a T2*-weighted gradient-echo sequence (TR = 2000 ms, TE = 30 ms, flip angle= 78°, matrix size 64 x 64, 3-mm in-plane resolution, 3-mm thick slices, .75-mm gap). For each participant, a high-resolution MP-RAGE structural volume was also acquired (TR = 1900, TE = 2.26, flip angle = 9°) with 176 sagittal slices, each 1 mm thick with 1 mm x 1 mm in-plane resolution.

2.5 fMRI Data Analysis:

Analysis was carried out using FSL's FMRI Expert Analysis Tool (FEAT) Version 5.1 (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl). Data preprocessing involved the following steps: motion correction (Jenkinson et al., 2002), brain extraction (Smith, 2002), slice timing correction, spatial smoothing with a 10-mm FWHM Gaussian kernel, high pass temporal filtering using Gaussian-weighted least-squares straight line fitting with sigma=90.0s, and pre-whitening (Woolrich et al., 2001). For each run, the BOLD response was modeled using a separate explanatory variable (EV) for each task condition (letter and location). For each task condition, the presentation design was convolved with a gamma function to produce an expected BOLD response. The temporal derivative of this time-course was then included in the model for each EV to capture any unexpected temporal shifting, and motion correction parameters were also included in the design as additional nuisance regressors. Data for each condition was then fitted to the model using FSL's implementation of the general linear model.

Each subject's statistical data was then warped into a standard space based on the MNI-152 atlas. We used FLIRT to register the functional data to the atlas space in three stages. First, functional images were aligned with the high-resolution co-planar T2-weighted image using a 6-degrees-of-freedom rigid-body warping procedure (Jenkinson and Smith, 2001;Jenkinson et al., 2002). Next, the co-planar volume was registered to the T1-weighted MP-RAGE using a 6-degrees-of-freedom rigid-body warp. Finally, the MP-RAGE was registered to the standard MNI atlas with a 12-degrees-of-freedom affine transformation, and then this transformation was refined using FNIRT nonlinear registration (Andersson et al., 2007b;a).

After analyzing each functional run for each subject, the two functional runs were combined using a fixed-effects analysis. Data from each subject was then passed into a higher-level analysis, which allowed comparisons within and between groups. Higher-level analysis was carried out using FLAME (FMRIB's Local Analysis of Mixed Effects), such that group-level effects were modeled using random effects (Behrens et al., 2003). Z (Gaussianised T/F) statistic images were thresholded using clusters determined by Z > 2.3 and a cluster significance threshold of p < .05 (corrected) (Worsley et al., 1992;Worsley et al., 1997). To examine individual differences, additional higher-level analyses were performed using behavioral (task accuracy, reaction time) and psychological assessment measures (ADHD symptom measures) as cross-subject regressors. These analyses were performed in FEAT, using a FLAME higher-level analysis that modeled the mean across subjects with one EV, and the demeaned behavioral correlate with a second EV. This resulted in whole brain maps for each regressor, that reflected the degree to which each voxel's activity correlated with that regressor across

subjects. Positive and negative contrast maps were thresholded according to the same Z > 2.3, cluster size p < .05 threshold.

2.6 Asymmetry Analysis

The purpose of our asymmetry analysis was twofold. First, we intended to characterize brain asymmetry in patients and controls in visual processing regions of the brain, i.e. those regions involved with the perceptual processing of the stimuli during the task. Second, we intended to probe how asymmetry in visual areas was related to processing in several key networks throughout the brain, several of which are suspected to play a role in ADHD. Recent work in neuroimaging has shown that the brain can be parceled into distinct networks based on intrinsic functional connectivity at rest, and that these networks may represent meaningful cognitive units (Smith et al., 2009;Van Dijk et al., 2010;Deco et al., 2011). Several of these networks show altered activity in individuals with ADHD (Castellanos and Proal, 2012;Cortese et al., 2012). Here, we follow Castellanos and Proal (2012) in employing the 7-network parcellation derived by Yeo et al. (2011). This network parcellation comes from analysis of resting-state fMRI from 1000 healthy adult subjects. Yeo et al. used a clustering algorithm to divide the brain into 7 non-overlapping networks on the basis of functional coupling, yielding a series of masks registered to the standard MNI-152 space that we used in our analysis. The 7 networks are depicted in Figure 1, and are known by their associations with the neuroimaging literature as the visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default networks. We computed the asymmetry index (AI) using voxels only within the visual network, and then correlated the AI with activation measures derived from each of the other networks. Note that these networks are non-overlapping, so voxels contributing to the visual network asymmetry are not included as part of any other network.

[Figure 1]

The most common approach to quantifying asymmetries of functional brain activation in the neuroimaging literature is to compute an asymmetry index (AI) as the ratio of the difference between left hemisphere activation (LHA) and right hemisphere activation (RHA) and the sum of activation in both hemispheres:

AI = (LHA - RHA)/(LHA + RHA) Positive AI values up to a maximum of+1 correspond to greater left hemisphere lateralization, while negative values with a minimum of -1 correspond to right hemisphere lateralization. However, there is little consensus as to how to compute the activation values that enter into this equation. Typically, voxels above a specified threshold are counted (Binder et al., 1996;Yetkin et al., 1998;Lehéricy et al., 2000;Holland et al., 2007;Tillema et al., 2008) or their statistical values are summed or averaged (Adcock et al., 2003;Jansen et al., 2006;Chlebus et al., 2007). Importantly, the choice of statistical threshold can have an effect on computed AI values (Binder et al., 1996;Deblaere et al., 2004;Wilke and Lidzba, 2007;Seghier, 2008;Suarez et al., 2009;Jones et al., 2011).

Two types of strategy for dealing with thresholding issues have recently emerged: 1) AI values are computed across a range of threshold values instead of a single threshold, and laterality curves are presented (Deblaere et al., 2004;Ruff et al., 2008;Seghier, 2008;Abbott et al., 2010;Strandberg et al., 2011), or 2) a single AI value is computed for each subject using the distribution of AI values across thresholds either to select a reasonable threshold, or to combine across thresholds using a weighting function (Fernández et al., 2001;Knecht et al., 2003;Calautti et al., 2007;Suarez et al., 2009;Niskanen et al., 2012). We have chosen to use a combination of both strategies. First, we computed a single AI value for each subject using a variation of Wilke and Lidzba's (2007) "adaptive threshold" technique. Here, for each subject, mean voxel intensity within the visual network mask was used as

the threshold to compute a visual network asymmetry index (VN-AI). Next, in order to thoroughly characterize activation asymmetries in each group we present asymmetry curves across a range of statistical thresholds.

Als were computed using the iBrain Laterality Toolbox (Abbott et al., 2010), using standard-space z-score images for each subject for each contrast. Z-score images were masked by the visual network mask from Yeo et al. (2011), and split into left and right halves along the midline of the brain. Then, to generate 'adaptive threshold' Als, individual's images were thresholded according to their mean voxel intensity within the visual network, and averages were computed within the left and right halves of the mask and subjected to the AI calculation described above. To generate AI curves, the same approach was utilized except that images were thresholded multiple times in 0.1 increments from z = 0.1 to z = 3.1 (corresponding to the z-distribution p-value of .001) creating 31 different AI scores.

Group differences in the adaptive-threshold based VN-AI were examined for each condition (allbaseline, letter-baseline, location-baseline) using univariate ANOVA (adjusted for age), and are considered our primary analyses of visual network asymmetry. AI-curves are included mainly for visual inspection; however, a 'principal components analysis' (PCA) based assessment of AI-curves is also presented as a secondary statistical approach.

Note that contrasts used in these asymmetry analysis (letter-baseline, location-baseline, all-baseline) involved comparison of conditions that had identical visual stimuli, and thus produced modest visual network activation. The primary adaptive threshold approach contends with this by normalizing each subject's AI score to their own mean signal strength within the visual network. However, with AI-curves, the maximum z-value shared by all subjects was z = 2.0. Thus, PCA based analysis of AI-curves targets z-values up to that point (i.e., that includes our full sample). We additionally report PCA based assessment of AI-curves up to z = 3.1, noting the reduction in sample sizes [sample sizes at z = 3.1: all-baseline (18 controls, 17 ADHD), letter-baseline (17 control, 16 ADHD), location-baseline (15 controls, 16 ADHD)].

Group differences across the twenty z-thresholds comprising the portion of the AI-curves that contained our full sample (from z = 0.1 to z = 2.0) were assessed using principal component analysis (PCA). Here, in order to reduce the number of dimensions from these 20 AI values (at each z-threshold) PCA was conducted on standardized variables (correlations) and components with eigenvalues greater than 1 extracted. Because the primary focus was on a single component explaining the variability in measures computed using different thresholds, no rotation was utilized. The same approach was used to assess group differences in AI-curve values ranging from z = 0.1 to z = 3.1 as a sensitivity analysis. In both cases, group differences in the resultant components were assessed using univariate ANOVA (adjusting for age).

Lastly, a key goal of the current study was to examine the relationship between hypothesized visual processing asymmetries in ADHD and identified functional networks suspected to play a role in the disorder (Yeo et al., 2011;Castellanos and Proal, 2012;Cortese et al., 2012). To do this, we computed the average z-score within each network mask for each subject (representing task-related activity within that network), and correlated these values (adjusted for age) with the adaptive-threshold based VN-AI values.

2.7 Behavioral Analyses

Group differences in letter and location task behavioral performance (accuracy, response time) were tested using univariate ANOVA (adjusted for age). Two additional analyses used partial correlations

(adjusted for age) to examine the relationship between letter task performance and VN-AI values, and mean signal intensity across the 6 extra-visual networks examined in this study.

2.8 Correlation Analyses

Where relevant, we used Fisher's R to Z test to statistically examine the difference between two correlations (Fisher, 1915). First, the correlations are transformed so that they are unbounded, using the inverse hyperbolic tangent function. Next, the difference between the transformed correlations is converted to a Z score based on the sample sizes, and then a p-value is obtained based on the Z score.

3. Results

3.1. Behavior

3.1.1 Task Performance

Controls exhibited better accuracy during the letter task, and a trend suggested the same during the location task (Table 2). Partial correlations (adjusted for age) indicated a speed-accuracy tradeoff during the letter task among ADHD subjects (accuracy correlated with response time: r=.74, p<.000), with a trend showing the same pattern during the location-task ( r=.39, p=.09). Fisher r-to-z test indicated that the ADHD speed accuracy tradeoff during the letter task was significantly different from controls (z= 2, p=04).

Table 2: Group Differences in Behavior

Behavior Measure Controls X se ADHD X se f df p

Letter Acc.(proportion correct) .91 .018 .86 .018 4.4 2, 39 .043

Letter RT (ms) 590 21 590 21 .007 2, 39 .93

Location Acc.(proportion correct) .93 .017 .88 .017 3.29 2, 39 .08

Location RT (ms) 490 17 500 17 .141 2, 39 .71

Univariate analysis of variance (adjusted for age) was used to examine group differences in tasks accuracy and response time (RT); x = estimated marginal means; se= standard error; RT values in milliseconds.

3.2 Standard Neuroimaging Results

3.2.1 Tasks - Baseline

All-Baseline: Both groups exhibited significant activation of the occipital cortex (extending into fusiform regions), but in opposite hemispheres (RH in ADHD, LH in controls). ADHD and controls also exhibited several overlapping activations in LH brain regions that included: supplementary motor, pre-central gyrus (superior lateral, inferior medial, plus inferior lateral in controls), and post-central gyrus bordering the supramarginal gyrus (extending into superior parietal cortex in controls). Lastly, ADHD subjects showed additional unique activations in the brain stem and cerebellum (Table 3, Figure 2).

Letter-Baseline: This contrast showed the same basic pattern as all-baseline except for the following: only controls activated supplementary motor cortex; significant occipital, brain stem, or cerebellum activations were not present in ADHD subjects; and ADHD subjects showed a unique activation in the left thalamus (Table 3, Figure 2).

Location-Baseline: This contrast showed the same basic pattern as all-baseline, as well as additional unique hippocampal activations among ADHD subjects (Table 3, Figure 2).

Direct comparisons between the groups did not show significant differences.

Table 3: Condition - Baseline: Within Group Effects

All-Baseline Control ADHD

Region Hem MNI Z-Val. Z-Val.

Supplementary Motor Cortex L -10, 2, 50 2.93 3.4

Superior Lateral Pre-central Gyrus L -40, -12, 66 6.03 5.63

Inferior Lateral Pre-central Gyrus L -44, -2, 28 4.05 None

Inferior Medial Pre-central Gyrus L -24, -12, 50 5.02 4.34

Supramarginal Gyrus L -38, -36, 38 4.83 3.93

Occipital Cortex L -36, -94, -4 5.62 None

Occipital Cortex R 40, -82, -4 None 4.29

Brain Stem Mid -4, -22, -16 None 3.72

Brain Stem Mid 0, -38, -24 None 4.07

Cerebellum Lobule VI R 26, -46, -28 None 5.04

Letter-Baseline Control ADHD

Supplementary Motor Cortex L -10, 2, 50 2.37 None

Superior Lateral Pre-central Gyrus L -40, -12, 66 5.24 4.87

Inferior Lateral Pre-central Gyrus L -44, -2, 28 3.80 None

Inferior Medial Pre-central Gyrus L -24, -12, 50 4.20 3.90

Supramarginal Gyrus L -38, -36, 38 4.44 3.93

Occipital Cortex L -36, -94, -4 5.17 None

Thalamus L -18, 20, 6 None 2.93

Location-Baseline Control ADHD

Supplementary Motor Cortex L -12, 2, 46 2.53 None

Superior Lateral Pre-central Gyrus L -40, -12, 66 5.16 4.90

Inferior Lateral Pre-central Gyrus L -46, -2, 26 3.60 None

Inferior Medial Pre-central Gyrus L -24, -12, 50 5.14 4.34

Supramarginal Gyrus L -36, -36, 36 4.50 3.66

Occipital Cortex L -32, -92, -4 4.98 None

Occipital Cortex R 34, -86, -4 None 4.0

Brain Stem Mid 2, -26, -18 None 3.31

Brain Stem Mid 0, -34, -24 None 3.47

Cerebellum Lobule VI R 24, -48, -28 None 3.91

Hippocampus L -32, -26, -14 None 3.53

Hippocampus R 32, -34, -4 None 2.60

Table shows significant within group activations per condition ordered along anterior-to-posterior axis. Hem.= hemisphere; L= left hemisphere; R= right hemisphere; Mid= activated voxel with x-coordinate between -5 and 5; MNI= Montreal Neurological Institute structural atlas coordinates (x, y, and z axis); Z-Val.= z-value indicating BOLD signal intensity at reported voxel; Significance determined using a voxelwise threshold of z = 2.3 and a cluster size probability of p < .05.

[Figure 2]

3.2.2 Task Comparisons

Letter - Location: There were no significant effects for this contrast.

Location - Letter: ADHD and control subjects exhibited several overlapping activations in brain regions associated with the DMN (medial prefrontal, medial parietal, and inferior parietal cortices). Additional unique activations were evident in subcortical regions among ADHD subjects, and within somatomotor regions among in controls (Figure 3 - see supplement part-2 for details). Direct comparison between the groups did not show significant differences.

[Figure 3]

3.3 Asymmetry Analyses

3.3.1 Adaptive Threshold Based Asymmetry

Analysis of group differences in the adaptive threshold based VN-AI showed controls had significantly greater leftward asymmetry for the all-baseline and letter-baseline contrasts (Table 4, Figure 4: A).

Table 4: Group Difference in Adaptive-Threshold Based Visual Network Asymmetry

fMRI Controls ADHD df

Contrasts X se X se I

All-Baseline .18 .06 -.06 .06 6.8 1,39 .013

Letter-Baseline .21 .06 -.03 .06 6.5 1,39 .014

Location-Baseline .09 .08 -.08 .08 2.2 1,39 .15

Univariate analysis of variance (adjusted for age) was used to examine group differences in the adaptive-threshold based visual network asymmetry indices (VN-AIs); x = estimated marginal means; se= standard error.

Although ADHD subjects did not differ on vocabulary and phonological measures, a trend effect (p = .06) suggested poorer reading, while a significant effect (p = .02) indicated poorer spelling compared to controls (Table 1). Importantly, the ADHD group mean for these standardized measures was not suggestive of clinical impairment (age normed standard mean for these measures = 100: ADHD reading = 102.4; ADHD spelling = 98.3). However, three ADHD subjects had reading and spelling scores within impairment ranges (i.e., 1.5-2 standard deviations below the standardized mean)- one of these learned English as second language. Asymmetry scores from these subjects did not present as outliers, and group differences in VN-AI effects remained significant with these subjects removed, or after co-varying for reading and spelling abilities.

3.3.2 Al-curves Based Asymmetry

Principal Components Analysis of Al-curves

For Al-curve values that contained the full sample (z = 0.1 to 2.0), a single principal component explained most of the variance in AI measures (> 91% for all conditions). For the all-baseline condition, only one component had an eigenvalue greater than 1; the remaining conditions each had a second component with an eigenvalue greater than 1, but accounting for a small amount of total variance (< 8%). Because the first component for all conditions explained the vast majority of variance in measures and due to our focus on identifying a single measure of asymmetry, only the first component was used in subsequent analyses. A similar pattern of results emerged when conducting the PCA with thresholds up to z = 3.1.

Group Differences in Al-curves

Consistent with primary VN-AI Analysis, analysis of PCA asymmetry components derived from thresholds z = 0.1 to 2.0 (i.e., the upper limit that included all subjects) showed controls had significantly greater leftward asymmetry than ADHD subjects during all-baseline and letter-baseline conditions (Table 5, Figure 4: B, C). In the letter-baseline condition, this effect remained significant across the full range of threshold values (i.e., up to z = 3.1) [F(1,39)=4.5, p=.04], even with the associated loss of statistical power (ADHD sample reduced 24%; control sample reduced 19%).

Table 5: Group Differences in AI-Curves

fMRI Controls ADHD df

Contrasts X se X se f p

All-Baseline .36 .20 -.36 .20 6.3 1,39 .016

Letter-Baseline .34 .20 -.34 .20 5.5 1,39 .024

Location-Baseline .23 .22 -.22 .22 2.1 1,39 .16

Univariate ANOVA (adjusted for age) was used to examine group differences in PCA components derived from asymmetry indices computed for each condition at thresholds ranging from z = .1 - 2. (see methods); x = estimated marginal means; se= standard error.

[Figure 4]

Additional analyses involving visual network asymmetry utilized the letter-baseline adaptive threshold VN-AI metric. Moreover, in the assessment of VN-AI association to extra-visual networks, extravisual-network values were derived exclusively from the letter-baseline condition where group differences in visual network asymmetry occurred. Also, note from figure 2 that at a group level the only significant increases in the cortex during tasks relative to baseline are within the visual network itself, and also in the left sensorimotor cortex. We attribute this to the relative similarity between our tasks and the active baseline. Hence, for correlation analyses involving extra-visual network BOLD signal, correlation effects largely reflect associations with variable degrees of task-associated deactivation.

3.3.3 VN-AI Association To Averaged Signal in Extra-Visual Networks

Partial correlation analysis (adjusted for age) showed that VN-AI in ADHD subjects was generally and positively correlated with signal in extra-visual networks during the letter task, with the effect surviving Bonferroni correction for somatomotor, ventral-attention, and default mode networks. In contrast, controls showed a pattern of negative (but mostly non-significant) associations between VN-AI and signal in extra-visual networks. One effect in controls (i.e., VN-AI correlation to LIM) was significant and survived Bonferroni correction. Fisher's r-to-z test indicated that all correlation effects were significantly different between groups (z-values ranged between 2.3 - 3.5, p-values ranged between .02 - .0005) (Table 6).

Table 6: Partial Correlations (Adjusted for Age) Between VN-AI And Averaged Signal Across

Asymmetry SOM * DAN * VAN * LIM * FPN * DMN *

ADHD r .66 .49 .66 .40 .52 .57

VN-AI P .001 .03 .001 .08 .02 .008

Control r -.29 -.22 -.19 -.63 -.27 -.31

VN-AI p .21 .34 .43 .003 .26 .18

Partial correlations (adjusted for age) were use to examine the association between averaged signal within target networks and VN-AI in each group; r-values are shown in the top of each cell; p-values are shown in the bottom of each cell; VN-AI= letter-baseline adaptive threshold visual network

asymmetry index ; SOM= somatomotor; DAN= dorsal attention network; VAN= ventral attention network; LIM= limbic network; FPN= frontoparietal network; DMN= default mode network; *= fisher's r-to-z test indicate significant group difference; Bold border= effect significant after multiple comparison adjustment (Bonferroni).

3.3.4 VN-AI Association to Voxelwise Signal Maps

There were no significant effects in controls. ADHD subjects showed exclusive positive association between VN-AI and BOLD response across multiple extra-visual brain regions, the majority of which fell within the DMN. The ADHD-exclusive associations produced significant group differences (Table 7). To help interpret these findings in relation to extra-visual networks, activation maps are also presented with color-coded overlays that demarcate extra-visual network boundaries (Figure 5).

Table 7: Visual Network Asymmetry Associa

Visual Network Asymmetry

Correlated With BOLD Signal ADHD A>C

Region Hem MNI Z-Val. Z-Val.

Frontal Pole (lateral) L -28, 52, 34 3.6 2.5

Frontal Pole (lateral) R 46, 34, -8 3.8 2.9

Frontal Pole (mid) L -10, 72, 6 3.5 3.7

Frontal Pole (mid) R 12, 64, 20 3.0 3.0

Superior Frontal Gyrus (lat) L -20, 16, 46 3.6

Superior Frontal Gyrus (mid) Mid 4, 50, 36 3.0 2.4

Inferior Frontal Gyrus R 48, 16, 16 3.4 3.7

Inferior Frontal Gyrus R 58, 34, 10 3.6 3.0

Frontal Operculum Cortex R 44, 16, 8 3.4 2.9

Frontal Operculum Cortex R 46, 0, 14 3.7 3.9

Paracingulate Gyrus Mid -2, 48, 14 3.1 2.9

Paracingulate Gyrus Mid-R 8, 48, 14 3.5 2.9

Precentral Gyrus (Inf) R 62, 4, 14 3.2

Postcentral Gyrus (Inf) R 64, -10, 24 3.1 2.8

Postcentral Gyrus (Sup) R 58, -10, 48 2.8 3.3

Temporal Pole R 46, 8, -40 3.0 2.8

Middle Temporal Gyrus (ant) R 48, -2, -28 3.8 3.8

Middle Temporal Gyrus (Inf) R 56, -28, -14 4.0 3.7

Inferior Temporal Gyrus R 52, -28, -22 2.6 3.3

Middle Temporal Gyrus (post) R 64, -22, -8 3.6 3.0

Temporal-occipital Cortex L -66, -52, -8

Temporal-occipital Cortex R 60, -44, 6 3.0 2.7

Superior Temporal Gyrus L -64, -10, 6 4.3 3.0

Superior Temporal Gyrus (Lat) R 62, -30, 2 3.4 3.2

Superior Temporal Gyrus (post) L -60, -40, 10 4.1 3.6

Superior Temporal Gyrus (post) R 44, -34, 4 4.1 3.2

Angular Gyrus L -62, -58, 30 4.0 3.5

Angular Gyrus R 62, -50, 28 3.7 3.9

Precuneus Cortex Mid-L -6, -52, 46 4.4

Precuneus Cortex Mid 4, -52, 48 3.5

Superior Parietal Lobule Mid-L -6, -56, 66 4.1

Superior Parietal Lobule Mid 4, -56, 62 3.2

ion with BOLD Signal During The Letter Task

Statistic

Table shows ADHD positive associations between visual network asymmetry (VN-AI) and extravisual-network brain regions during the letter task. A>C= indicates significantly greater positive association in ADHD versus controls. Results are ordered along anterior-to-posterior axis; Hem.=

hemisphere; L= left hemisphere; R= right hemisphere; Mid= activated voxel with x-coordinate between -5 and 5; MNI= Montreal Neurological Institute structural atlas coordinates (x, y, and z axis); Z-Val.= z-value indicating BOLD signal intensity at reported voxel; Significance determined using a voxelwise threshold of z = 2.3 and a cluster size probability of p < .05.

[Figure 5] 3.4 Symptoms

These analyses are performed exclusively in ADHD subjects, as there was insufficient variability in symptoms metrics in controls to justify examination. Symptom data reflect DSM-IV criteria for inattentive and hyperactive subscales, obtained during K-SAD-PL semi-structured interviews (with mother and child participant). Due to the relative importance of symptom effects in ADHD, we've provided scatter plots to help guide interpretation of correlation findings.

3.4.1 ADHD Symptoms Association to VN-AI

Partial correlations (adjusted for age) indicated no relationship between symptoms and VN-AI during the letter-baseline condition.

3.4.2 ADHD Symptoms Association to Extra-visual Networks

Partial correlations (adjusted for age) indicated no relationship between symptoms and extra-visual networks during letter task (i.e., letter-baseline). However, trend level effects suggested possible associations between inattention and limbic (r=.39, p=.09), and default mode (r=.42, p=.06) network activation. During the location task, partial correlations (adjusted for age), indicated a positive association between inattentive symptoms and DMN activation (r=.51, p=.02) (i.e., more inattentive symptoms = more DMN activation), however, this effect did not survive Bonferroni correction for multiple testing (see supplement part-3 for scatter plot).

3.4.3 ADHD Symptoms Associations to Voxelwise Signal Maps

For all-baseline and location-baseline contrast, ADHD subjects exhibited several positive associations between inattentive symptoms and BOLD signal in medial prefrontal brain regions. There were no associations for hyperactive symptoms (Table 8, Figure 6). Also, see supplement part-3 for scatter plot of inattentive symptoms correlation to BOLD signal for above-threshold voxels depicted in the location-baseline condition.

Table 8: Symptoms Association With BOLD Signal

Inattentive Symptoms Statistic

Correlated With BOLD Signal ADHD

Region Hem MNI Z-Val.

All-Baseline

Frontal Pole L -14, 56, 8 3.70

Frontal Pole Mid -4, 62, 8 3.51

Frontal Pole Mid-R 8, 58, 10 3.06

Frontal Medial Cortex Mid 2, 48, 14 2.73

Paracingulate Gyrus L -16, 46, -2 4.42

Paracingulate Gyrus Mid 0, 46, 8 3.33

Paracingulate Gyrus R 16, 54, 2 4.11

Location-Baseline

Frontal Pole L -14, 56, 10 4.06

Frontal Pole Mid -4, 64, 10 2.90

Frontal Pole R 14, 60, 4 4.36

Frontal Pole R 10, 62, 30 3.08

Frontal Medial Cortex L -18, 48, -4 4.08

Cingulate Gyrus (anterior) Mid-L -8, 36, 6 3.58

Table shows ADHD positive associations between inattentive symptoms and BOLD response in allbaseline and location-baseline conditions. Hem.= hemisphere; L= left hemisphere; R= right hemisphere; Mid= activated voxel with x-coordinate between -5 and 5; MNI= Montreal Neurological Institute structural atlas coordinates (x, y, and z axis); Z-Val.= z-value indicating BOLD signal intensity at reported voxel; Significance determined using a voxelwise threshold of z = 2.3 and a cluster size probability of p < .05.

[Figure 6]

3.5.2 Task Performance Association with VN-AI

Partial correlation analysis (adjusted for age) demonstrated that neither group showed any association between letter task accuracy and VN-AI. Both groups showed non-significant associations between letter task RT and VN-AI [ADHD (r=24, p=32), controls (r=-.35, p=.13)]. Fisher's r-to-z test indicated that the difference between the groups correlations was trending toward significance (z=1.83, p=.067).

3.5.3 Task Performance Association with Extra-Visual Networks

Letter-Baseline: In ADHD subjects, letter task performance was not significantly correlated with extravisual networks. In controls, accuracy was negatively correlated with the limbic (r= -.48, p=.03) and default mode (r= -.57, .009) networks, but these effects did not survive adjustment for multiple testing (i.e., Bonferroni). Still, Fisher's r-to-z test indicated they were different from ADHD subjects [LIM (z=1.96, p=05); DMN (z=2, p=045)]. Also, note that the DMNeffect in controls was just shy of the Bonferroni corrected cut-off (p=.009 vs. a =.008). In controls, letter task RT was positively correlated with SOM activation (r= .45, p=.04), but this did not survive Bonferroni correction, and was not significantly different from ADHD subjects.

Location-Baseline: In ADHD subjects, location task performance was not significantly associated with extra-visual networks. In controls, accuracy was negatively correlated with DMN activation (r= -.48, p=.03), but this effect did not survive Bonferroni correction, and was not significantly different from controls (Fisher's r-to-z test: z=1.68, p=.09).

3.5.4 Task Performance Association with ADHD Symptoms

Partial correlations (adjusted for age) indicated no relationship between ADHD symptoms and behavioral performance.

3.5.5 Task Performance Association to Voxelwise Signal Maps

Response Time: Controls exhibited several positive associations between response time and BOLD signal in somatomotor brain regions. There were no group differences (see supplement part-4 for details).

Accuracy: Controls showed negative associations between tasks accuracy and BOLD signal in brain regions understood to reflect DMN activation. These associations also produced significant group differences (Table 9, Figure 7) for the ADHD minus Controls contrast.

Table 9: Task Behavioral Accuracy Association with BOLD Signal

Accuracy Correlated Statistic

With BOLD Signal Controls A>C

Region Hem MNI Z-Val. Z-Val.

Letter-Baseline

Frontal Pole L -18, 54, 40 -3.20

Frontal Pole Mid 2, 54, -24 3.01

Frontal Orbital Cortex (inferior) L -10, 6, -20 3.83

Frontal Medial Cortex R 10, 42, -16 3.31

Subcallosal Cortex Mid-L -6, 28, -12 -3.45 3.42

Subcallosal Cortex Mid 4, 26, -14 -3.13 3.50

Cingulate Gyrus (anterior) Mid 0, 42, 8 -3.41 4.05

Paracingulate Gyrus L -14, 38, 18 -3.24

Occipital Pole L -26, -100, -16 3.11

Cerebellum L -12, -80, -48 3.72

Cerebellum Mid-R 6, -86, -42 3.43

Location-Baseline

Frontal Pole (inferior) R 14, 36, -26 -4.16 4.47

Frontal Pole (superior) R 22, 44, 38 -3.95

Frontal Pole Mid-L -8, 64, -2 -3.37 2.94

Frontal Pole L -22, 58, 24 -2.98

Frontal Orbital Cortex L -12, 22, -24 4.05

Frontal Orbital Cortex R 10, 30, -22 -4.88 4.71

Subcallosal Cortex Mid-R 8, 26, -22 -4.68 4.83

Middle Frontal Gyrus L -34, 24, 44 -3.28

Superior Frontal Gyrus L -18, 26, 52 -3.19

Frontal Medial Cortex Mid -2, 52, -26 -3.02 2.74

Cingulate Gyrus (anterior) Mid 0, 28, 14 -3.60

Cingulate Gyrus (anterior) Mid 2, 36, 20 -3.44

Paracingulate Gyrus (ant) R 12, 38, 24 -3.36

Paracingulate Gyrus (dorsal) Mid 2, 20, 48 -3.06

Table shows significant negative associations between tasks accuracy and BOLD response in letterbaseline and location-baseline conditions. A>C= indicates significantly greater association in ADHD versus controls. Hem.= hemisphere; L= left hemisphere; R= right hemisphere; Mid= activated voxel with x-coordinate between -5 and 5; MNI= Montreal Neurological Institute structural atlas coordinates (x, y, and z axis); Z-Val.= z-value indicating BOLD signal intensity at reported voxel; Significance determined using a voxelwise threshold of z = 2.3 and a cluster size probability of p < .05.

[Figure 7]

Post-hoc Analysis

For the purpose of data interpretation two additional post-hoc analyses were performed. Univariate ANOVA (adjusted for age) was used to examine whether there were group differences in the averaged DMN activation. There were no group differences in any task condition (p>.69). Partial correlations (adjusted for age) were used to examine association between location task VN-AI and extra-visual networks (averaged signal) during the location task. There were none in either group.

4. Discussion

The current study used recently developed fMRI methods to replicate and further examine identified abnormal rightward biased information processing in ADHD. Our task presented four-letter word stimuli and required subjects to detect a uniquely colored red letter and decide whether it was an 'A' or not (letter task), or whether it was on the left or right (location task). Initial within-group analyses revealed a pattern of left lateralized visual cortical activity in controls, but right lateralized visual cortical activity in ADHD children. Our primary direct analyses of visual network asymmetry (VNA) confirmed that atypical rightward VNA was present in ADHD children and significantly different from controls in the letter task and overall. This finding adds to the growing literature that identifies abnormal information processing to be a key factor in ADHD. Moreover, in conjunction with our previous work (see introduction), it further specifies that ADHD abnormal information processing includes atypical increased weighting of RH versus LH contribution. Indeed, we have now demonstrated this characteristic using behavioral laterality (Hale et al., 2005;Hale et al., 2006), EEG asymmetry (Hale et al., 2009b;Hale et al., 2010a;Hale et al., 2010b), and here by examining asymmetry of fMRI BOLD signal.

Through our secondary aim, we additionally demonstrated that ADHD subjects' rightward VNA during the letter task was atypically associated with reduced DMN activation. Recall that positive VNA scores reflect leftward asymmetry, with the reverse also true (i.e., negative VNA scores reflect rightward asymmetry). We found, in two separate analyses of BOLD signal in the letter task, that ADHD subjects exhibited an atypical positive correlation between VNA and DMN signal. This indicates that leftward VNA is associated with greater DMN activation, and that rightward VNA is associated with reduced DMN activation. Given ADHD subjects' atypical rightward VNA during the letter task, we focus our discussion of DMN findings on the link between rightward VNA and reduced DMN activation in ADHD. Regardless of the directionality, this and additional network findings importantly demonstrate that atypical rightward VNA in ADHD is associated with multiple distributed brain systems, including previously implicated large-scale networks and frontal brain regions.

Abnormal Visual Information Processing and ADHD

Functional abnormalities in the visual cortex have proven to be a key feature of ADHD (Cortese et al., 2012), with abnormal visual cortical structure identified (Wang et al., 2007;Ahrendts et al., 2011), and abnormal early-stage sensory information processing well established (Weiler et al., 2002;Willcutt et al., 2005;Shanahan et al., 2006;McGrath et al., 2011;Johnstone et al., 2013). This literature implicates ADHD deficits for both visual discriminations and categorization functions. The current study, along with our previously discussed findings (see introduction), adds an important novel element to this topic- that is, abnormal information processing in ADHD involves atypical increased weighting of RH versus LH contribution to visual sensory information processing.

Hemispheric specialization of visual cortical functions notably includes LH specialization for linguistic stimuli, and RH specialization for faces (for review: Rossion et al., 2003). However, RH specialization is also reported for bottom-up functions, such as: detection of sequence breaking novel objects (Bubic et al., 2011), automatic assessment of object relevance (Downar et al., 2001), automatic perceptual/integrative category learning (Zeithamova and Maddox, 2007;Roser et al., 2011), aesthetic analysis (Di Dio et al., 2007), and within-category feature discrimination (Hammer et al., 2010). Furthermore, RH specialization is well established for top-down task-directed attention functions, such as: vigilance, sustained and selective attention (Heilman et al., 1986;Pardo and Raichle, 1991;Corbetta et al., 1993;Ruff et al., 2009;Summerfield and Egner, 2009;Zanto et al., 2011). Our current study finding of rightward VNA in ADHD suggest some form of increased weighting these right-lateralized mechanisms.

Right Hemisphere Contributions to VNA

The above noted right-lateralized brain functions reflect two classes of sensory information processing: self-directed top-down and automatic bottom-up. Within these domains, we can further distinguish processing that supports fast stimulus identification (i.e., categorization) versus in-depth sensory analysis. In the top-down domain, this reflects applied effort to identify/categorize a stimulus, or to scrutinize a stimulus's details (Hochstein and Ahissar, 2002). In the bottom-up domain, this reflects mechanisms that automatically alert us to behaviorally relevant content in our surroundings, or that support fluid sensory-immersive experience (Corbetta and Shulman, 2002;Bar, 2003). In total, we conceptualize four variant domains of RH contribution to visual processing: 1) task-directed categorizations, 2) task-directed scrutiny of details, 3) bottom-up automatic categorizations, and 4) bottom-up sensory-immersive. A key premise of the current study, and our previous work, is that complex task-directed actions heavily rely on the first of these (i.e., task-directed categorizations) to support fast-efficient perceptual identification of task-stimuli, using the minimal sensory exposure required to do so. We refer to this as 'task-specialized' sensory information processing, and conceptualize it to include varying mixtures of sustained, selective, and vigilance related attentional functions, depending on the nature of a given task.

We've previously hypothesized (Hale, 2014) that any form of reduced ability for this task-specialized manner of visual information processing is likely to coincide with a proportional increased expression of non task-specialized forms, resulting in greater possible expression of: a) unneeded scrutiny of visual details, b) attentional shifting to off-task content, and/or c) task-inappropriate orientation toward sensory-immersive processing. The net effect of this is expected to be an increased exposure to visual content beyond what is strictly required to perform task operations. This is conceptualized as 'visual sensory overflow' in relation to task objectives. Our model postulates that this circumstance may underlie increased RH contribution to visual sensory processing in ADHD (for full model description see: Hale, 2014).

To examine this thesis, our current study was designed so that task conditions differentially engaged the task-specialized manner of visual information processing, but were otherwise perceptually identical. The letter task required subjects to identify a nominated target 'A', and distinguish it from other letters. This was expected to tax RH mechanisms that support top-down selective attention (Heilman et al., 1986;Pardo and Raichle, 1991;Corbetta et al., 1993;Ruff et al., 2009;Summerfield and Egner, 2009;Zanto et al., 2011), which is a key aspect of task-specialized visual information processing. In contrast, the spatial condition did not require maintenance of a nominated target, or making categorical judgments about discrete items, and as such, was not expected to tax selective

attention. Thus, to the extent rightward VNA in ADHD reflects reduced efficiency for task-specialized visual processing, we expected it to be maximally expressed during the letter condition. This supposition was born out. However, a non significant pattern of rightward VNA in ADHD was also generally apparent. This suggests that in addition to an applied attentional effect underlying rightward VNA in ADHD, there may also be some form of default bias toward perceptual versus linguistic processing. This notion is further discussed below.

Left Hemisphere Contributions to VNA

The above discussion addresses possible sources of atypically increased RH contribution to visual processing, however, our current study did not, strictly speaking, uncover such an effect. We demonstrated increased rightward VNA in ADHD. This indicates a relative increased weighting of RH versus LH visual cortical contributions. An additional component of our proposed model (Hale, 2014) is that with optimal functioning of task-specialized visual information processing, efficient perceptual-level encoding of task-stimuli is expected to be quickly followed by the translation of perceptual content into verbal articulatory codes that facilitate updating of task-directives in verbal working memory (Hickok and Poeppel, 2007;Hale, 2014). According to this view, optimum performance of this system should be indexed by minimal resources having to be utilized at early perceptual stages. That is, information processing should move as efficiently as possible from perceptual-level to verbal categorization functions.

Consistent with this, both developmental and adult studies show a transfer of right to left hemisphere processing of visual information that coincides with the learning of new visual items and their name codes (Seger et al., 2000;Franklin et al., 2008). Ostensibly, with greater familiarity the requirement for perceptual-level analyses is reduced (likely due to greater use of predictive imagery), which allows faster transitioning from perceptual to verbal-categorical stages. Furthermore, recent work shows that transitioning into LH dominant modes of processing during linguistic operations is a function of RH inhibition rather than increased LH activation (Seghier and Price, 2011). These studies highlight that the relative efficiency of verbal sensory encoding is partly a function of earlier perceptual-level operations. In this vein, we suggest that the currently observed lack of normal leftward VNA in ADHD during the letter task, and associated worse accuracy, is likely a secondary consequence of abnormal perceptual stage processing. This view seems to also align with the identified slower naming speeds in ADHD, which occur absent any overt linguistic impairment (Semrud-Clikeman et al., 2000;Tannock et al., 2000;Weiler et al., 2000;Nigg et al., 2002;Rucklidge and Tannock, 2002;Stevens et al., 2002;Brock and Christo, 2003;Willcutt et al., 2005). It is also consistent with our previous study that showed ADHD adults' impaired ability for detecting word stimuli could be completely normalized by altering attentional parameters (Hale et al., 2006).

Abnormal Left-Right Integration

Evidence of increased rightward VNA in ADHD is well aligned with identified reduced posterior corpus callosum size (Seidman et al., 2005;Valera et al., 2007) and abnormal function in ADHD (Chabot and Serfontein, 1996;Barry et al., 2005;Clarke et al., 2007;Rolfe et al., 2007). In fact, the specific callosal region implicated in ADHD (the splenium) connects left and right visual cortices (Putnam et al., 2010). This callosal region undergoes increases of myelination across development, resulting in greater interhemispheric EEG synchrony (particularly in alpha 8-12 Hz), and capacity to regulate lateralized visual cortical functions (Knyazeva, 2013). Moreover, these changes include a progression from right-to-left dominance of visual cortical processing, and are suggested to reflect plastic tuning in response to childhood and adolesent maturation of visual ability (Seger et al.,

2000;Franklin et al., 2008;Knyazeva, 2013). These findings suggest that abnormal VNA in ADHD might reflect some form of deviant maturation of posterior callosal functioning that bears on interhemispheric coordination of visual functions. Identified abnormal posterior EEG coherence in ADHD may be consistent with this view (Chabot and Serfontein, 1996;Barry et al., 2005;Clarke et al., 2007).

Another aspect of collosal functioning that is perhaps relevant to ADHD and our current finding has to do with the directionality of interhemipsheric transfer. Although previously considered asymmetric, a recent study showed that a greater proportion of splenial collosal fibers project right-to-left than left-to-right (Putnam et al., 2010). This is consistent with normally observed faster right-to-left callosal transfer times (Rolfe et al., 2007;Putnam et al., 2010). However, a study by Rolfe et al. (2007) has indicated that ADHD subjects exhibit a reversed pattern of faster left-to-right transfer times (in combined types), and/or atypically slow right-to-left transfer (in inattentive types), suggesting a possible increased reliance on (or dominance of) RH visual cortical contribution. Moreover, a recent structural imaging study has reported larger RH visual cortical volumes in ADHD (Wang et al., 2007), and our previous laterality work demonstrated that ADHD adults are beter able to inhibit pre-potent LH based stimulus resposivity than controls (Hale et al., 2005). Together, these studies support the view that there may be some form of default increased reliance on, or dominance of, RH contribution to visual sensory information processing in ADHD. If true, this default or state-independent aspect may function as a separate but additive factor to the more applied attentional effects previously discussed.

Visual Network Asymmetry and Extra-Visual Networks

Our previous work indicated that atypical rightward asymmetry in ADHD is sensitive to top-down modulation of attention and brain-state orientation (Hale et al., 2005;Hale et al., 2006;Hale et al., 2007), and others have shown ADHD cognitive impairments can be sensitive to alterations in motivation (Modesto-Lowe et al., 2013). However, findings of structural and functional deviations at rest also clearly implicate more fixed or state-independent abnormal brain function in ADHD (Hale et al., 2000;Bush et al., 2005;Valera et al., 2007). In an attempt to further clarify the nature of abnormal brain function underlying rightward VNA in ADHD, the current study examined the association between VNA and extra-visual networks, several of which are implicated in ADHD (Castellanos and Proal, 2012;Cortese et al., 2012). In particular, our aim was to examine the assocation between rightward VNA and default mode network (DMN) function.

DMN function has been widely investigated in recent years, with multiple studies linking it to ADHD (Castellanos and Proal, 2012;Cortese et al., 2012). Although previously characterized as a resting or task-negative network (Fox et al., 2005), studies have now indicated an active role in internally directed self-referential aspects of cognition, also highlighting that its interactive dynamics with other networks are more flexible and circumstance-specific than previously understood (Uddin et al., 2009;Spreng, 2012;Wang et al., 2012;Kragel and Polyn, 2013). To this end, Wang et al., (2012) showed that most aspects of the DMN increased coherence during a word-picture matching task, with the lone exception being connectivity between bilateral posterior cingulum and RH inferior parietal cortex. They suggested greater on-task coherence of DMN reflects internal task-mediating processes, and concluded that DMN function is engaged during tasks, but in specific fashions rather than absolutely suppressed. Hampson et al. (2006) was an early proponent of a similar view, suggesting DMN function is engaged during cognitive challenges to facilitate or monitor cognitive performance. Furthermore, Uddin et al. (2009), using Granger Causality Analysis, has demonstrated DMN direct modulation of task-positive networks. Finally, a recent study by Yakushev et al. (2013) has shown a

link between DMN integrity and verbal working memory ability. Together, these findings raise the intriguing possibility that abnormal DMN function in ADHD might be associated with a reduced capacity to orchestrate the internal aspects of task-directed cognition (e.g., planning, initiating, maintaining, and updating task directives) (Desimone and Duncan, 1995;Baddeley et al., 2001;0livers et al., 2011;de Fockert, 2013). If true, this could underlie a general reduced capacity for task-directed brain functions in ADHD, including poor task-directed visual sensory information processing.

The current study showed that VNA in ADHD was more generally and robustly associated with extravisual networks compared to controls. The generality of these associations may fit with the above view insomuch as abnormal DMN function in ADHD might be synonymous with having a less stable task-directed neural architecture (Bressler and Tognoli, 2006;Hale, 2014), with associated poorer on-task modulation of task-positive networks. This suggests that ADHD task-directed brain functioning may generally occur in a less coordinated or piecemeal manner. If true, ADHD subjects may have needed to more often adjust effort between component 'internal verbally weighted', and 'external perceptually weighted' operations, resulting in a greater general association between VNA and extra-visual networks during our letter task.

In addition to the above noted general effects, a critical role for DMN function in ADHD was also directly indicated. VNA association to DMN signal was one of three effects that survived Bonferroni correction for multiple testing. Moreover, DMN signal showed unique exclusive association to both inattentive symptoms and behavioral performance in ADHD subjects. Inattentive symptoms showed a positive association to medial anterior aspects, while ADHD subjects showed no behavioral association to DMN function, with controls exhibiting the expected pattern of greater accuracy with reduced DMN activation (also involving medial anterior aspects). Moreover, and consistent with the above discussion, these effects occurred mainly during the more difficult letter-task condition, which ostensibly placed greater demands on internal task processing, possibly including an increased requirement for DMN modulation of task-positive networks (Uddin et al., 2009).

With regard to the directionality of effects, our findings showed a pattern of positive association between the VN-AI metric and all extra-visual networks examined. This means that among ADHD subjects leftward VNA was associated with stronger network signal, while rightward VNA was associated with reduced network signal. Given our primary finding of increased rightward VNA in ADHD, the latter aspect is most relevant. That is, atypical increased rightward VNA in ADHD during the letter task was associated with reduced network signal, most notably for the default mode and ventral attention networks. Reduced DMN activation occurs with active externally oriented processing (Spreng, 2012). Reduced VAN activation has been linked to having a fixed or stable attentional set (Downar et al., 2001;Zeithamova and Maddox, 2007;Bubic et al., 2011;Roser et al., 2011). This suggests that rightward VNA in ADHD during letter discriminations may reflect some form of externally oriented task-adaptive or compensatory processing. This speculation is supported by the trend effect showing ADHD subjects were faster with greater rightward asymmetry (and slower with leftward asymmetry), while controls showed an opposite pattern. Moreover, ADHD subjects exhibited a unique and robust speed-accuracy tradeoff during the letter task, which may be consistent with effortful compensatory processing.

Additional Considerations

As noted, medial prefrontal aspects of the DMN network were associated with ADHD inattentive symptoms and task performance. This brain region has been identified as a source of top-down regulation of the brain-stem locus coeruleus (Yeo et al., 2011), which via dense noradrenergic

projections to the RH, is critical for managing transitions between controlled and flexible attention and cognitive sets (Aston-Jones and Cohen, 2005;Howells et al., 2012). Moreover, noradrenergic projections via the locus coeruleus have also been shown to play an active role in modulating RH visual cortical functions (Grefkes et al., 2010). Given abnormal norepinephrine (Del Campo et al., 2011) DMN (Castellanos and Proal, 2012), and RH visual cortical volume in ADHD (Wang et al., 2007), the above noted circuits suggest a possible mechanism by which abnormal DMN function may be linked to both disregulated attention-state setting (e.g., exploratory versus task-oriented), and greater RH visual cortical contribution in ADHD.

DMN influence over applied attention may also occur. As noted, Wang et al., (2012) reported that most aspects of DMN circuitry increased coherence during a word-picture matching task, with an exception being connectivity between bilateral posterior cingulum and the RH inferior parietal cortex. Franzen et al. (2013) reported reduced resting-state DMN connectivity in ADHD subjects between this same DMN posterior cingulum region and RH inferior parietal cortex. Given the well-established role of RH inferior parietal cortex in top-down attentional functions (for review see: Singh-Curry and Husain, 2009), it is interesting to consider that this aspect of DMN circuitry might somehow undermine ADHD subjects' ability to direct task-specialized visual sensory information processing. Finally, the possibility that abnormal DMN function impacts both attention-state and applied attention mechanisms in ADHD may be consistent with findings presented by Uddin et al. (2009) showing bipartite DMN functions, with differential impacts on RH inferior parietal and anterior regions.

Conclusions

The current study demonstrated rightward VNA in ADHD during a simple letter discrimination task. This result, in conjunction with our previous findings, adds an important novel consideration to the growing literature identifying abnormal visual sensory information processing in ADHD. We expect rightward VNA reflects increased perceptual engagement of task-extraneous content, and that this occurs with any form of reduced ability for top-down task-directed visual sensory information processing. The current study also identified that rightward VNA in ADHD was atypically and robustly associated with multiple extra-visual network systems, namely the DMN and VAN. Rightward VNA in ADHD was associated with reduced activation in these networks, possibly indicating some form of task-adaptive compensatory processing. Moreover, we also identified abnormal DMN associations to ADHD inattentive symptoms and behavioral performance during our letter task. We postulate that abnormal DMN function in ADHD may index a general reduced capacity to induce and/or maintain a task-adaptive neural architecture, with negative cascading effects resulting in less efficient task-directed perceptual encoding of visual stimuli, and associated increased rightward VNA.

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Figure Titles/Caption

Figure 1

Title: Figure 1: The 7-network Parcellation

Caption: Figure reproduced from Castellanos & Proal, 2012, Trends in Cognitive Sciences Figure 2

Title: Figure 2: Task Conditions - Baseline: Within-Group Effects

Caption: Within-group analysis of BOLD signal revealed unique LH occipital activations in controls (yellow), and unique RH occipital activations in ADHD subjects (red). Several common activations were also evident (orange), such as LH: supplementary motor, pre-central gyrus (superior lateral, inferior medial), and post-central gyrus boarding the supramarginal gyrus. ADHD subjects showed additional unique subcortical activations that included: thalamus, brainstem, cerebellum, and hippocampus. Images are thresholded using a voxelwise threshold of z = 2.3 and a cluster size probability of p < .05.

Figure 3

Title: Figure 3: Location - Letter: Within-Group Effects

Caption: Within group analysis of location-letter contrast revealed several overlapping activations among ADHD (red) and control subjects (yellow) likely reflective of greater DMN activation during the location versus letter tasks. ADHD subjects showed additional unique activations in subcortical structures, while controls showed additional unique activations within somatomotor network regions. Overlap between the two groups is shown in orange. Images are thresholded using a voxelwise threshold of z = 2.3 and a cluster size probability of p < .05.

Figure 4

Title: Figure 4: Analysis of Visual Network Asymmetry

Caption: Showing adaptive threshold based visual network asymmetry indices (AN-VI) (panel A), and asymmetry curves Al-curves (panels B, C, D). For adaptive threshold indices '*' signifies group difference in all-baseline and letter-baseline conditions. For Al-curves (B, C, D) '*' signifies group difference in primary PCA component computed from z-thresholds ranging from .1 up to 2.0 (allbaseline, letter-baseline), and up to 3.1 (letter-baseline). At the threshold z=3.1, n sizes reduced to the following: all-baseline (18 controls, 17 ADHD), letter-baseline (17 control, 16 ADHD), location-baseline (15 controls, 16 ADHD); Note: positive asymmetry values = leftward asymmetry.

Figure 5

595 Title: Figure 5: ADHD Visual Network Asymmetry Association with BOLD Signal During the

596 Letter Task

.598 Caption: ADHD subjects showed exclusive positive associations between visual network asymmetry

599 (VN-AI) during the letter task and BOLD response across multiple brain regions, likely reflective of

600 DMN activation. These ADHD exclusive associations produced significant group differences. The

601 upper row shows ADHD positive association maps. The lower row shows the same maps color-coded .602 to depict extra-visual networks; SOM= somatomotor; DAN= dorsal attention network; VAN= ventral .603 attention network; LIM= limbic network; FPN= frontoparietal network; DMN= default mode

.604 network. Images are thresholded using a voxelwise threshold of z = 2.3 and a cluster size probability of

605 p < .05.

608 Figure 6

610 Title: Figure 6: Inattentive Symptoms Correlated With BOLD Signal In ADHD Subjects

612 Caption: Positive associations between inattentive symptoms and BOLD responses in medial .613 prefrontal regions that are associated with the DMN were observed in ADHD subjects for the all-614 baseline and location-baseline conditions. Images are thresholded using a voxelwise threshold of z = .615 2.3 and a cluster size probability of p < .05. 616

619 Figure 7

621 Title: Figure 7: Task Behavioral Accuracy Association With BOLD Signal

623 Caption: In both conditions controls exhibited negative associations between task accuracy and BOLD .624 signal in superior-frontal and anterior medial brain regions (blue). We also found significant group 625 differences in these same regions for the ADHD minus Controls contrast (purple). Images are .626 thresholded using a voxelwise threshold of z = 2.3 and a cluster size probability of p < .05.

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