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Additive and interactive effects of attention-deficit/hyperactivity disorder and tic disorder on brain connectivity

  • Joseph Jurgiel
    Affiliations
    Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA
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  • Makoto Miyakoshi
    Affiliations
    Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
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  • Andrea Dillon
    Affiliations
    Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA
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  • John Piacentini
    Affiliations
    Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA
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  • Sandra K. Loo
    Correspondence
    Correspondence to: Sandra Loo, Ph.D. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles 760 Westwood Plaza, A7-456, Los Angeles, CA 90095, USA; Phone: (310)-825-9204; Fax: (310)-206-4446.
    Affiliations
    Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA
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Open AccessPublished:October 27, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.10.003

      Abstract

      Background

      Attention-deficit/hyperactivity disorder (ADHD) and persistent tic disorder (PTD) are two neurodevelopmental disorders that frequently co-occur. Contributions of each disorder to cognitive and behavioral deficits have been reported. In this paper, we tested three models of pathophysiology for the two disorders: additive, interactive, and phenotypic using resting state connectivity associated with each disorder separately and together.

      Methods

      Participants were 148 children (55 with ADHD, 33 with ADHD + PTD, 27 with PTD, and 33 healthy controls), ages 8-12 years old. Following diagnostic interviews and behavioral assessment, participants underwent a 128-channel electroencephalography (EEG) recording. Resting state, cortical source-level effective connectivity was analyzed among the four groups using a 2x2 factorial design with factors of ADHD (with/without) and PTD (with/without).

      Results

      ADHD diagnosis was the primary driver of cognitive and behavioral deficits, while deficits associated with PTD were primarily with thought problems and internalizing problems when compared to controls. Subadditive effects were observed in co-occurring ADHD + PTD for parent-rated behavioral problems and cognitive functions. Aberrant effective connectivity was primarily associated with ADHD, with lower posterior and occipital-frontal connectivity, while children with PTD exhibited greater left postcentral to precuneus connectivity. Weaker ADHD-related connectivity was associated with more severe behavioral problems, including internalizing behaviors, thought problems, and working memory.

      Conclusions

      Similar to general behavioral deficits, aberrant resting state neural connectivity in pediatric ADHD and PTD combines additively in co-occurring cases. The present findings support ADHD as a focus of treatment in comorbid cases, given the driving role in both behavioral and neurophysiological deficits.

      Keywords

      Introduction

      Attention-deficit/hyperactivity disorder (ADHD) and persistent tic disorder (PTD) are neurodevelopmental disorders that develop during childhood, with an ADHD prevalence of approximately 6% (
      • Polanczyk G.
      • de Lima M.S.
      • Horta B.L.
      • Biederman J.
      • Rohde L.A.
      The Worldwide Prevalence of ADHD: A Systematic Review and Metaregression Analysis.
      ) and PTD prevalence of around 1-2% (
      • Scahill L.
      • Specht M.
      • Page C.
      The prevalence of tic disorders and clinical characteristics in children.
      ) in pediatric populations. Despite their differing symptomatology, the two disorders show particularly high rates of comorbidity with one another, with upwards of 50% of children with PTD meeting an ADHD diagnosis (
      • Freeman R.
      • Consortium T.
      Tic disorders and ADHD: Answers from a world-wide clinical dataset on Tourette syndrome.
      ) and approximately 20% of children with ADHD possessing a tic disorder (
      • Banaschewski T.
      • Neale B.M.
      • Rothenberger A.
      • Roessner V.
      Comorbidity of tic disorders & ADHD.
      ).
      Numerous transdiagnostic studies have been conducted comparing ADHD and PTD phenotypes to investigate the overlapping nature of the disorders and their unique influences on cognitive performance and behavioral functioning. Converging evidence among pediatric studies suggest ADHD as the primary driver of most neuropsychological deficits in comorbid cases (
      • Shin M.-S.
      • Chung S.-J.
      • Hong K.-E.M.
      Comparative Study of the Behavioral and Neuropsychologic Characteristics of Tic Disorder With or Without Attention-Deficit Hyperactivity Disorder (ADHD).
      ), along with greater impact on psychosocial outcomes in multiple domains than PTD (
      • Spencer T.
      • Biederman J.
      • Coffey B.
      • Geller D.
      • Wilens T.
      • Faraone S.
      The 4-Year Course of Tic Disorders in Boys With Attention-Deficit/Hyperactivity Disorder.
      ). ADHD has also been strongly linked to both externalizing and internalizing problems, while PTD has been primarily related to internalizing behaviors (
      • Roessner V.
      • Becker A.
      • Banaschewski T.
      • Rothenberger A.
      Psychopathological profile in children with chronic tic disorder and co-existing ADHD: additive effects.
      ). However, some problematic externalizing behaviors, such as rage attacks, are observed in PTD (
      • Conte G.
      • Valente F.
      • Fioriello F.
      • Cardona F.
      Rage attacks in Tourette Syndrome and Chronic Tic Disorder: a systematic review.
      ), and some studies have indicated that ADHD with PTD results in more severe internalizing issues (anxiety, worsened social functioning, reduced quality of life) relative to ADHD alone (
      • Lin Y.-J.
      • Lai M.-C.
      • Gau S.S.-F.
      Youths with ADHD with and without tic disorders: Comorbid psychopathology, executive function and social adjustment.
      ,
      • Poh W.
      • Payne J.M.
      • Gulenc A.
      • Efron D.
      Chronic tic disorders in children with ADHD.
      ). These behavioral findings support the idea of an additive model of pediatric ADHD and PTD pathophysiology, with ADHD and PTD being two separate diagnostic entities. This is in contrast to two other models that have been developed regarding combined ADHD and PTD diagnoses (
      • Simpson H.A.
      • Jung L.
      • Murphy T.K.
      Update on Attention-Deficit/Hyperactivity Disorder and Tic Disorders: A Review of the Current Literature.
      ). One is an interactive model, which suggests that comorbid cases are a unique diagnostic entity, as has been observed in young adults with comorbid ADHD and PTD (
      • Müller O.
      • Rothenberger A.
      • Brüni G.L.
      • Wang B.
      • Becker A.
      Questioning the long-term stability of the additive model in comorbid CTD+ADHD - The transition from childhood to adulthood.
      ). The other is a phenotypic model, which considers comorbid cases to be a phenotypic subgroup with a pathological basis of either ADHD or PTD.
      These hypothesized models have been primarily explored in behavioral studies. For example, evaluations of sleep alterations in ADHD and PTD found unique additive characteristics from each disorder in comorbid groups (
      • Kirov R.
      • Kinkelbur J.
      • Banaschewski T.
      • Rothenberger A.
      Sleep patterns in children with attention-deficit/hyperactivity disorder, tic disorder, and comorbidity.
      ). Studies evaluating task-based performance deficits have reported conflicting findings regarding the combined impact of the two disorders on cognitive performance. Some cognitive control studies have noted performance deficits in both ADHD with PTD and ADHD without PTD (
      • Shephard E.
      • Jackson G.M.
      • Groom M.J.
      The effects of co-occurring ADHD symptoms on electrophysiological correlates of cognitive control in young people with Tourette syndrome.
      ,
      • Greimel E.
      • Wanderer S.
      • Rothenberger A.
      • Herpertz-Dahlmann B.
      • Konrad K.
      • Roessner V.
      Attentional Performance in Children and Adolescents with Tic Disorder and Co-Occurring Attention-Deficit/Hyperactivity Disorder: New Insights from a 2 × 2 Factorial Design Study.
      ), while others have found deficits only in ADHD without PTD, with comorbid groups performing similar to controls (
      • Morand-Beaulieu S.
      • Smith S.D.
      • Ibrahim K.
      • Wu J.
      • Leckman J.F.
      • Crowley M.J.
      • Sukhodolsky D.G.
      Electrophysiological signatures of inhibitory control in children with Tourette syndrome and attention-deficit/hyperactivity disorder.
      ). Another study involving reinforcement learning demonstrated that in children with PTD, the presence of ADHD did not impact acquisitional learning but did impact modifying learned behaviors (
      • Shephard E.
      • Jackson G.M.
      • Groom M.J.
      Electrophysiological correlates of reinforcement learning in young people with Tourette syndrome with and without co-occurring ADHD symptoms.
      ). An impaired ability to modify learned behaviors may have a particularly negative impact on response to behavioral and pharmacotherapy treatment regimens for PTD (
      • McGuire J.F.
      • Piacentini J.
      • Brennan E.A.
      • Lewin A.B.
      • Murphy T.K.
      • Small B.J.
      • Storch E.A.
      A meta-analysis of behavior therapy for Tourette Syndrome.
      ,
      • Weisman H.
      • Qureshi I.A.
      • Leckman J.F.
      • Scahill L.
      • Bloch M.H.
      Systematic review: Pharmacological treatment of tic disorders – Efficacy of antipsychotic and alpha-2 adrenergic agonist agents.
      ).
      Electroencephalography (EEG) and neuroimaging approaches provide methods to characterize and assess the neural mechanisms underlying ADHD and PTD. However, there have been limited transdiagnostic studies comparing atypical neurophysiological attributes of the two disorders using these approaches thus far. Investigations into reward processing have indicated a relationship between ADHD severity and nucleus accumbens activation, irrespective of PTD presence (
      • Akkermans S.E.A.
      • van Rooij D.
      • Naaijen J.
      • Forde N.J.
      • Boecker-Schlier R.
      • Openneer T.J.C.
      • et al.
      Neural reward processing in paediatric Tourette syndrome and/or attention-deficit/hyperactivity disorder.
      ). Decreased mid-line theta power has been noted during a NoGo task in pediatric ADHD without PTD but not in comorbid cases, suggesting subadditive effects of the two disorders (
      • Morand-Beaulieu S.
      • Smith S.D.
      • Ibrahim K.
      • Wu J.
      • Leckman J.F.
      • Crowley M.J.
      • Sukhodolsky D.G.
      Electrophysiological signatures of inhibitory control in children with Tourette syndrome and attention-deficit/hyperactivity disorder.
      ). Another pediatric study observed an association between greater spontaneous frontal theta during selective attention and the presence of ADHD, but stronger early event-related theta activity in co-occurring ADHD + PTD (
      • Yordanova J.
      • Heinrich H.
      • Kolev V.
      • Rothenberger A.
      Increased event-related theta activity as a psychophysiological marker of comorbidity in children with tics and attention-deficit/hyperactivity disorders.
      ). This could suggest that simple or resting state tasks may involve additive effects, while complex cognitive tasks may result in interactions between disorders . Functional connectivity studies have suggested atypical network interactions between the default-mode and frontoparietal networks within each disorder individually (
      • Konrad K.
      • Eickhoff S.B.
      Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder.
      ,
      • Castellanos F.X.
      • Aoki Y.
      Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development.
      ,
      • Sutcubasi B.
      • Metin B.
      • Kurban M.K.
      • Metin Z.E.
      • Beser B.
      • Sonuga-Barke E.
      Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis.
      ,
      • Wen H.
      • Liu Y.
      • Rekik I.
      • Wang S.
      • Chen Z.
      • Zhang J.
      • et al.
      Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children.
      ). Graph theory analyses have indicated diagnostic group differences in local efficiency and clustering coefficients, with lower connectivity among frontoparietal network nodes in PTD relative to ADHD and lower connectivity among default mode network nodes in PTD relative to controls (
      • Openneer T.J.C.
      • Marsman J.-B.C.
      • van der Meer D.
      • Forde N.J.
      • Akkermans S.E.A.
      • Naaijen J.
      • et al.
      A graph theory study of resting-state functional connectivity in children with Tourette syndrome.
      ). Frontoparietal network connectivity in the comorbid ADHD and PTD group was also observed to be more similar to the PTD group than the ADHD group, suggesting tics may have a stronger influence on this network.
      Further exploration of these disorders, particularly from a whole-brain network approach, could help provide important insight into the unique region-to-region communication patterns associated with each disorder. While functional connectivity measures reflect correlations between brain regions, it does not reveal the underlying directionality of influences (
      • Friston K.J.
      Functional and Effective Connectivity: A Review.
      ). As of yet, these directional neural dynamics have remained unexplored in the context of co-occurring ADHD and PTD. Further parsing of the neural mechanisms of each disorder may be beneficial in understanding behavioral, cognitive, and treatment response in the respective disorders. The present study aimed to fill this gap by examining EEG-based cortical source-level, resting state effective connectivity in a sample of 8-12 year old children. A 2x2 factorial design was utilized with factors of ADHD (present/not present) and PTD (present/not present), providing four groups for the analysis: ADHD (without PTD), PTD (without ADHD), co-occurring ADHD + PTD, and healthy controls. We expected to replicate prior studies suggesting ADHD as the primary driver of behavioral and cognitive deficits, and hypothesized that atypical neural communication would also be principally driven by ADHD, with deficits primarily in default mode network nodes due to the resting state paradigm. We also hypothesized that these deficits would follow the additive ADHD/PTD model supported by prior clinical pediatric studies, with the two disorders presenting unique contributions to atypical regional communication.

      Methods

      Participants

      The study consisted of 148 children aged 8-12 years old: 55 with ADHD (without PTD), 33 with comorbid ADHD + PTD, 27 with PTD (without ADHD), and 33 typically developing healthy controls (HC). The ADHD participants were recruited and recorded during the baseline visit of a clinical trial before any treatment was administered. The PTD, comorbid PTD + ADHD, and HC groups were recorded as part of an EEG study on PTD. Both studies ran concurrently and were assessed using the same diagnostic protocols and EEG equipment. Participants were recruited through community advertisements and internet postings, and from an academic medical center anxiety and tic disorder clinic. Parents and participants received verbal and written explanations of study criteria and provided informed consent/assent prior to screening and any study procedures. All study procedures were approved by the local Institutional Review Board.
      All children underwent semi-structured diagnostic interviews, cognitive testing, and an EEG recording during a single experimental session. Children in all groups were male or female aged 8-12 years and have an estimated Full Scale IQ ≥85 based on the Wechsler Abbreviated Scale of Intelligence (WASI) (

      Wechsler D (1999): Wechsler Abbreviated Scale of Intelligence WASI: Manual. Pearson/PsychCorpl.

      ). The Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS-PL) (
      • Kaufman J.
      • Birmaher B.
      • Brent D.
      • Rao U.
      • Flynn C.
      • Moreci P.
      • et al.
      Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial Reliability and Validity Data.
      ) was administered to assess for DSM-5 diagnoses of ADHD, PTD and all potential co-morbid disorders. Additional inclusion criteria for children in the ADHD groups were an ADHD-IV Rating Scale (ADHD-RS) score ≥24 (

      DuPaul GJ, Power TJ, Anastopoulos AD, Reid R (1998): ADHD Rating Scale—IV: Checklists, Norms, and Clinical Interpretation. New York, NY, US: Guilford Press, pp viii, 79.

      ) and Clinical Global Impression Severity (CGI-S) score ≥4 (

      Guy W (1976): Assessment Manual for Psychopharmacology. U.S. Department of Health, Education, and Welfare.

      ) and for children in PTD groups a score ≥15 on the Yale Global Tic Severity Scale (YGTSS) (
      • Leckman J.F.
      • Riddle M.A.
      • Hardin M.T.
      • Ort S.I.
      • Swartz K.L.
      • Stevenson J.
      • Cohen D.J.
      The Yale Global Tic Severity Scale: Initial Testing of a Clinician-Rated Scale of Tic Severity.
      ). Broad-band behavioral functioning was assessed using the Child Behavior Checklist (CBCL) (

      Achenbach T (1991): Manual for The Child Behavior Checklist/4-18 and 1991 Profile. Univ Vt Dep Psychiatry.

      ) and the Behavior Rating Inventory of Executive Function (BRIEF) (
      • Gioia G.A.
      • Isquith P.K.
      • Retzlaff P.D.
      • Espy K.A.
      Confirmatory Factor Analysis of the Behavior Rating Inventory of Executive Function (BRIEF) in a Clinical Sample.
      ). Exclusion criteria for all diagnostic groups included a history of major depression, autism spectrum disorder, bipolar disorder, psychosis, mania, seizure disorder, panic disorder, head injury resulting in concussion, or suicidality. Children in the patient groups (ADHD, PTD, ADHD + PTD) were required to discontinue stimulant medications for 24 hours prior to participation. Healthy controls were excluded if they had any major Axis 1 diagnosis or were on a psychoactive medication.

      EEG Recording

      Participants performed a 5-minute, eyes-open resting state condition while EEG was recorded using a 128-channel Electrical Geodesics (EGI, Eugene, Oregon) GES300 system. Data were referenced to Cz and sampled at 1000Hz with an electrode impedance threshold of 50kΩ (per manufacturer standard). Electrode coordinates were obtained using Polhemus Inc. digitizer software, using the nasion and preauricular notches as anatomical reference locations.
      Continuous EEG data was processed and cleaned using EEGLAB software (
      • Delorme A.
      • Makeig S.
      EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
      ). Data were down sampled to 250Hz and filtered using a 0.5-55Hz bandpass filter. Artifact Subspace Reconstruction (https://github.com/sccn/clean_rawdata) (
      • Kothe C.A.
      • Makeig S.
      BCILAB: a platform for brain–computer interface development.
      ) was performed via the EEGLAB plug-in clean_rawdata() to remove channel artifacts and interpolate non-stationary high amplitude bursts, as well as remove channels with over 5 seconds of flat signal and those poorly correlated (r < 0.85) with adjacent channels. Data were then further down sampled to 100Hz and adaptive mixture independent component analysis (AMICA) (

      Palmer JA, Kreutz-Delgado K, Makeig S (2011): AMICA: An Adaptive Mixture of Independent Component Analyzers with Shared Components. 15.

      ) was utilized to decompose scalp-level data into independent source level components (ICs). Locations of source dipoles were then estimated using Fieldtrip (
      • Oostenveld R.
      • Fries P.
      • Maris E.
      • Schoffelen J.-M.
      FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.
      ). Non-neural ICs were rejected using the EEGLAB plug-in ICLabel (
      • Pion-Tonachini L.
      • Kreutz-Delgado K.
      • Makeig S.
      ICLabel: An automated electroencephalographic independent component classifier, dataset, and website.
      ) if they displayed over 15% residual variance or “brain” was not the highest probability source. To facilitate proper fitting of the multivariate autoregressive (MVAR) model for connectivity, subjects were limited to their top 15 ranked ICs.

      Connectivity Analysis

      Effective connectivity aims to estimate the direction and magnitude of information flow (which is used interchangeably with connectivity) between brain regions, whereby past values from Region A provide supplemental predictive information about the future activity of a separate Region B (
      • Nolte G.
      • Ziehe A.
      • Nikulin V.V.
      • Schlögl A.
      • Krämer N.
      • Brismar T.
      • Müller K.-R.
      Robustly Estimating the Flow Direction of Information in Complex Physical Systems.
      ). Through the use of an MVAR model, effective connectivity estimates can be extended beyond a single pair of regions by modeling past activity from several regions to estimate causal relationships between each pair. Here, we have utilized an MVAR model fitted from the Vieira-Morf algorithm. For each subject, information flow between cortical sources (ICs) was estimated in the form of renormalized partial directed coherence (rPDC) (
      • Schelter B.
      • Timmer J.
      • Eichler M.
      Assessing the strength of directed influences among neural signals using renormalized partial directed coherence.
      ), in order to access the frequency-rich content of EEG activity, for 25 log-scaled frequencies from 2 to 30Hz using the EEGLAB plug-in groupSIFT (
      • Loo S.K.
      • Miyakoshi M.
      • Tung K.
      • Lloyd E.
      • Salgari G.
      • Dillon A.
      • et al.
      Neural activation and connectivity during cued eye blinks in Chronic Tic Disorders.
      ). The renormalized version of PDC was selected as it allows for comparisons between different magnitudes of PDC values. Additionally, cortical source connectivity, rather than channel level, was utilized to localize underlying neural mechanisms (rather than scalp mechanisms) as well as avoid volume conduction issues inherent among measures of channel-level connectivity (

      Brunner C, Billinger M, Seeber M, Mullen TR, Makeig S (2016): Volume Conduction Influences Scalp-Based Connectivity Estimates. Front Comput Neurosci 10. Retrieved August 21, 2022, from https://www.frontiersin.org/articles/10.3389/fncom.2016.00121

      ).
      To facilitate comparisons of connectivity across subjects, we then estimated whole brain activity from the cortical source ICs. Using the approach of Loo et al. (2019), a 3-D Gaussian kernel (full width at half maximum of 20mm, truncated at 3σ) spatially smoothed dipoles from point sources into probabilistic densities. The head model was then segmented into 76 regions of interest (ROIs) based on the automated anatomical labeling (AAL) atlas (
      • Tzourio-Mazoyer N.
      • Landeau B.
      • Papathanassiou D.
      • Crivello F.
      • Etard O.
      • Delcroix N.
      • et al.
      Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain.
      ). Within each subject, activity at each ROI was estimated based on the weighted-average contribution of Gaussian smoothed dipoles. Connections which maintained 80% of unique subjects were included in the analysis.
      A mass-univariate analysis was implemented, where for each frequency bin of each connection, a 2x2 factorial analysis of variance (ANOVA) was performed on the four groups, with factors of ADHD (with vs. without ADHD) and PTD (with vs. without PTD). Statistics (F values) for main effects of ADHD and PTD as well as interaction effects were observed and stored separately. Since connectivity values among neighboring frequency bins are not fully independent, a cluster-based approach was used, which aimed to find frequency ranges (e.g., groups of adjacent frequency bins) where significant connectivity effects were present. To facilitate this, connectivity values in each array were first masked at p < 0.05 significance, and groups of neighboring frequency bins with surviving F statistics were then summed together to form F-statistics-weighted mass of clusters (FMOCs), representing frequency ranges of significant effects.
      Cluster-level correction was implemented for control of generalized familywise error rate using permutation tests (i.e., to determine which connectivity effects survive statistical correction) (
      • Groppe D.M.
      • Urbach T.P.
      • Kutas M.
      Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review.
      ). To build surrogate statistics representing the null hypothesis (no group effect), group labels of rPDC arrays were iteratively shuffled (N=1000) and statistical comparisons were repeated for each iteration for each graph edge. Observing across all edges, the second maximum FMOC was extracted from each iteration to form a unique null distribution for each effect, resulting in three null distributions (main effect of ADHD, main effect of PTD, their interaction). For each effect, true FMOCs were compared to their respective null distribution using a p < 0.05 significance threshold, and surviving results were analyzed.

      Statistical Analysis

      All statistics were performed in the R programming environment. Factorial ANOVAs (with factors of ADHD and PTD) were first evaluated on CBCL, BRIEF, and attentional performance (measured using congruent and incongruent trials from a modified Erikson arrow-flanker task (
      • Eriksen B.A.
      • Eriksen C.W.
      Effects of noise letters upon the identification of a target letter in a nonsearch task.
      )) scores to examine behavioral deficits associated with each disorder. Pairwise group differences in behavioral measures, as well as connectivity measures, were observed using Tukey’s HSD post-hoc tests.
      To aid functional interpretations and assess relationships between neural connectivity and severity of behavioral deficits, Pearson correlations were calculated between significant connectivity measures and CBCL/BRIEF scores. False discovery rate (FDR) correction was implemented at a p=0.05 level to control type 1 error rate.

      Results

      As described in Table 1, demographics across the four participant groups were well-matched in terms of age and sex. Estimated intelligence (IQ) was significantly different across the groups, with lower, albeit still within the average range, IQ in the ADHD group relative to controls. A total of 10 participants in the comorbid group and two participants in the PTD group were on non-stimulant psychotropic medication. Four participants in the comorbid group were taking a prescribed stimulant, which was discontinued 24 hours prior to their visit. No participants in the ADHD group were taking any prescribed psychotropic or stimulant medications.
      Table 1Sample demographics and behavioral scores
      MeasureGroup means (SD)Diagnostic Effect (F value unless otherwise indicated)Post-hoc (p<0.05)
      ADHDADHD + PTDPTDHCADHDPTDInteraction
      DemographicsSubjects (N)55332733---
      Age9.9 (1.3)9.7 (1.6)10.0 (1.5)9.6 (1.5)<1--
      Sex35M:20F24M:9F21M:6F19M:14Fχ2 = 3.5--
      IQ108 (13)111 (17)113 (13)117 (14)2.8*A < H
      OCD (N)-13-----
      GAD (N)996-χ2 = 1.53--
      ODD (N)1760-χ2 = 10.9***A > T
      YGTSS-29.1 (7.6)24.4 (8.3)-5.2*-
      CBCLAnxiety/Depression59.1 (8.7)62.2 (11)53.7 (4.7)51.7 (3.5)32.1***3.7<1A, A+ > T, H
      Withdrawal57.4 (8.0)59.5 (8.4)53.2 (6.0)51.5 (3.6)24.3***2.5<1A, A+ > T, H
      Somatic Complaints57.7 (7.3)59.5 (9.7)55.5 (6.7)52.5 (4.2)13.2***3.5<1A, A+ > H
      Social Problems59.5 (7.6)59.5 (7.4)51.9 (4.2)51.4 (2.2)56.1***<1<1A, A+ > T, H
      Thought Problems62.9 (8.2)66.8 (8.5)58.9 (7.6)52.0 (3.9)52.1***16.5***1.4A, A+, T > H
      Attention Problems74.2 (9.2)68 (11.3)53.8 (5.1)52.5 (3.5)179.7***4.5*6.9*A > A+ > T, H
      Rule-breaking58.1 (7.3)55.7 (6.3)52.2 (3.3)52.1 (3.6)26.6***2.01.6A > T, H
      Aggression62.8 (9.5)59.1 (8.2)52.1 (3.5)52.0 (3.7)56.7***2.82.3A, A+ > T, H
      Internalizing Problems57.5 (10.9)60.5 (11.8)49.6 (10.7)43.5 (8.8)32.1***5.5*0.7A, A+ > T, H
      Externalizing Problems60.3 (10.4)55.9 (10.7)44.8 (9.3)44.6 (9.0)45.7***2.01.7A, A+ > T, H
      Total Problems64.0 (7.9)62.5 (9.3)48.3 (9.7)42.5 (9.9)67.7***<15.4*A, A+ > T, H
      BRIEFInhibition70.0 (14.4)62.1 (11.3)48.5 (8.4)47.7 (9.1)141.6***4.5*4.8*A > A+ > T, H
      Shifting61.0 (12.1)62.6 (13.2)49.0 (8.3)43.6 (5.0)92.7***1.11.4A, A+ > T, H
      Emotional Control62.9 (15)58.4 (10.4)50.0 (10.4)44.9 (7.4)36.1***<15.6*A, A+ > T, H
      Initiation66.7 (10.4)61.4 (10.5)47.7 (11.5)44.1 (6.4)48.8***<16.9**A, A+ > T, H
      Planning/Organization68.7 (10.3)61.8 (11.4)48.3 (9.1)44.5 (6.3)129.0***5.1*10.6***A > A+ > T, H
      Organization of Materials59.2 (8.6)59.2 (10.3)48.2 (7.6)46.6 (6.0)79.9***<1<1A, A+ > T, H
      Task Monitoring66.3 (9.7)59.4 (13.6)49.7 (10)43.3 (10.9)32.8***<112.5***A > A+ > T, H
      Working Memory73.5 (7.9)64.9 (9.8)48.9 (8.2)45.1 (6.6)89.9***6.1*19.2***A > A+ > T, H
      Flanker PerformanceCongruent Accuracy (%)72 (18)77 (18)77 (13)80 (18)2.2<11.22--
      Congruent RT (ms)590 (108)552 (103)506 (78)565 (99)4.8*6.7*0.35A > T
      Congruent RT Variability (ms)168 (55)137 (40)127 (33)141 (44)7.0**8.9**1.0A > A+, T
      Incongruent Accuracy (%)58 (17)60 (19)59 (12)67 (19)1.96<11.86--
      Incongruent RT (ms)627 (132)581 (138)556 (105)612 (141)1.064.7*<1--
      Incongruent RT Variability (ms)190 (64)158 (39)151 (44)152 (43)7.75**4.9*2.9A > A+, T, H
      Note. SD = standard deviation, ADHD+/A+ = ADHD + persistent tic disorder, PTD /T = persistent tic disorder, HC/H = healthy control, A = ADHD, OCD = obsessive-compulsive disorder, ANOVA = analysis of variance, GAD = generalized anxiety disorder, ODD = oppositional defiant disorder, YGTSS = Yale Global Tic Severity Scale, CBCL = Child Behavior Checklist, BRIEF = Behavior Rating Inventory of Executive Function, RT = reaction time. *P<0.05, **P<0.01, ***P<0.001

      Behavioral Findings

      Factorial ANOVA analyses on clinical scores revealed a significant main effect of ADHD for higher problem scores on all subscales of the CBCL and BRIEF measures, and a significant main effect of PTD for greater thought problems and internalizing problems. Interaction effects between the two disorders were observed for CBCL subscales: Attention Problems and Total Problems, as well as BRIEF scales of Inhibition, Initiation, Planning/Organization, Task Monitoring, and Working Memory. Post-hoc comparisons indicated the ADHD and comorbid groups to be more clinically impaired than PTD and HC groups on most measures. However, for several measures (attention problems, inhibition, planning/organization, task monitoring, and working memory) the comorbid group showed reduced clinical impairment compared to the ADHD group (although still significantly higher scores than PTD and HC groups), suggesting an apparent subadditive combination of the two disorders on parent-rated cognitive functioning.
      No significant effects of diagnosis were observed on task performance for flanker task accuracy, however, main effects of ADHD and PTD were noted for congruent and incongruent trial reaction time (RT) and RT variability. Notably, these effects were in opposite directions, with PTD diagnosis being associated with faster RT and lower RT variability and an ADHD diagnosis with slower RT and higher RT variability. Post-hoc Tukey HSD tests on performance measures indicated pairwise group differences between the ADHD and PTD groups (ADHD > PTD) in congruent trial RT as well as RT variability for both trial types. These directional effects also resulted in a significant difference in RT variability between the ADHD and comorbid groups, aligning with the prior observed subadditivity of behavioral measures of attention and inhibition.

      Effective Connectivity Findings

      Utilizing 80% subject inclusion criteria for connections, 254 connections were included in the analysis. Of these, several aberrant neural connections with significant main effects of ADHD and PTD were detected, along with one atypical connection common to both disorders (Fig. 1). Frequency ranges of these connectivity effects are shown in Table 2. Connections with a main effect of ADHD exhibited lower information flow among occipital-frontal (right calcarine to right superior frontal cortex (F(1,122)=10.2, p=0.002)) and several right to left intra-occipital connections: right cuneus to left cuneus (F(1,124)=7.0, p=0.009), right cuneus to left superior occipital (F(1,123)=8.2, p=0.005), and right cuneus to left midoccipital cortex (F(1,122)=10.5, p=0.002). Children with ADHD also displayed lower information flow from the precuneus to left midtemporal cortex (F(1,119)=8.1, p=0.005). Pairwise group comparisons (Tukey HSD) indicated the ADHD group had significantly lower connectivity compared to non-ADHD subgroups (Table 2).
      Figure thumbnail gr1
      Figure 1Resting state connectivity by diagnosis. ADHD was associated with lower resting state information flow among posterior and occipital-frontal connections, while persistent tic disorder (PTD) diagnosis was related to higher left postcentral to precuneus connectivity. Lower connectivity in ADHD and PTD groups from the left occipital cortex to right precuneus compared to healthy controls (HC), was common among both disorders. L = left, R = right, Sup = superior. Visualization was created using BrainNet Viewer (

      Xia M, Wang J, He Y (2013): BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics ((P. Csermely, editor)). PLoS ONE 8: e68910.

      ).
      Table 2Diagnostic group differences in connectivity (p-values)
      Frequency RangeADHD+-ADHDPTD-ADHDHC-ADHDPTD-ADHD+HC-ADHD+HC-PTD
      Effect of ADHD
      R Calcarine to R Sup Frontal2 to 17Hz0.870.430.060.170.02*0.83
      R Cuneus to L Cuneus2 to 8Hz0.990.550.04*0.690.120.73
      R Cuneus to L Sup Occipital2 to 8Hz0.980.40.02*0.70.120.71
      R Cuneus to L Midoccipital2 to 9Hz0.940.01*0.140.10.470.82
      R Precuneus to L Midtemporal7 to 18Hz0.540.02*0.120.410.840.88
      Effect of PTD
      L Postcentral to R Precuneus12 to 30Hz0.530.03*0.980.510.820.12
      Effect of Both ADHD & PTD
      L Cuneus to R Precuneus7 to 17Hz0.990.930.03*0.990.03*0.02*
      Note. Tukey HSD p-values. ADHD+ = ADHD + persistent tic disorder, PTD = persistent tic disorder, HC = healthy control, L = left, R = right, Sup = superior. *P<0.05
      A main effect of PTD was higher information flow from the left postcentral cortex to right precuneus (F(1,127)=6.0, p=0.02). PTD was additionally associated with lower connectivity from the left cuneus to right precuneus (F(1,133)=4.57, p=0.03), with ADHD also showing trend level (F(1,133)=3.21, p=0.07) differences. Pairwise post-hoc comparisons indicated the HC group had significantly higher connectivity relative to the three affected groups, suggesting lower connectivity between left cuneus to right precuneus to be a shared deficit among the two disorders. No significant diagnostic interaction effects of connectivity were observed.

      Correlations between connectivity and behavior

      Correlation analyses revealed significant relationships between connections with a main effect of ADHD and several CBCL/BRIEF measures after FDR correction. These included broad band CBCL measures of internalizing problems (anxiety/depression, thought problems), total problems, as well as more cognition-based BRIEF measures of initiation, organization, and working memory. All correlations were negative in direction, indicating greater clinical and cognitive deficits were associated with lower information flow among posterior and occipital-frontal connections. The connection with a main effect of PTD (i.e., right precuneus to left postcentral cortex), did not show any significant relationship with behavioral deficits.
      Table 3Pearson correlations between network connections and behavioral measures.
      ConnectionBehavioral Scorer [CI (adjusted)]
      R Calcarine to R Sup FrontalAnxiety/Depression-0.26* [-0.46, -0.05]
      Thought Problems-0.29* [-0.48, -0.07]
      Total Problems-0.26* [-0.45, -0.05]
      R Cuneus to L CuneusInternalizing Problems-0.26* [-0.45, -0.04]
      (BR) Initiation-0.25* [-0.44, -0.04]
      R Cuneus to L Sup Occipital(BR) Initiation-0.26* [-0.45, -0.05]
      R Cuneus to L MidoccipitalInternalizing Problems-0.32* [-0.51, -0.11]
      Total Problems-0.26* [-0.46, -0.05]
      (BR) Initiation-0.26* [-0.45, -0.04]
      (BR) Planning/Organization-0.25* [-0.45, -0.03]
      (BR) Working Memory-0.26* [-0.45, -0.05]
      R Precuneus to R MidtemporalTotal Problems-0.26* [-0.45, -0.04]
      (BR) Planning/Organization-0.27* [-0.46, -0.05]
      (BR) Working Memory-0.31* [-0.50, -0.10]
      Note. Pearson correlations are false discovery rate (FDR) corrected. Behavioral scores are from the Child Behavior Checklist unless noted as being from the Behavioral Rating Inventory of Executive Function (BR). CI = confidence interval, L = left, R = right, Sup = superior. *P<0.05

      Discussion

      This is the first EEG study to utilize a resting state effective connectivity approach to examine shared and distinct neural features of ADHD and PTD. The findings indicate ADHD as the primary driver of behavioral, cognitive, and neural connectivity deficits in these two frequently co-occurring disorders. In the connectivity analyses, all but one of the group differences were related to ADHD, particularly lower information flow among posterior and occipital-frontal connections. Both behavioral deficits and aberrant resting state neural connectivity in pediatric ADHD and PTD combines additively in co-occurring cases. Negative correlations between information flow and behavioral scores (CBCL/BRIEF scores) suggest that lower connectivity is associated with higher clinical impairment.
      Consistent with findings from the present study, prior clinical reports examining co-occurring ADHD and PTD have implicated ADHD as the primary contributor to behavioral and cognitive performance deficits among comorbid pediatric populations (
      • Shin M.-S.
      • Chung S.-J.
      • Hong K.-E.M.
      Comparative Study of the Behavioral and Neuropsychologic Characteristics of Tic Disorder With or Without Attention-Deficit Hyperactivity Disorder (ADHD).
      ,
      • Lin Y.-J.
      • Lai M.-C.
      • Gau S.S.-F.
      Youths with ADHD with and without tic disorders: Comorbid psychopathology, executive function and social adjustment.
      ). Despite a main effect of PTD for increased thought problems and internalizing problems, the latter of which has been reported previously (
      • Roessner V.
      • Becker A.
      • Banaschewski T.
      • Rothenberger A.
      Psychopathological profile in children with chronic tic disorder and co-existing ADHD: additive effects.
      ), significantly increased impairment was not observed in comorbid children. Total YGTSS score, however, was significantly higher in the comorbid group compared to the PTD group, suggesting more severe tics among those with comorbid ADHD and PTD. In contrast, several behavioral measures had significantly reduced clinical severity in comorbid children (relative to ADHD without PTD), including measures of attention, inhibition, planning/organization, task monitoring, and working memory. Additionally, while flanker task reaction time variability was significantly higher in ADHD and in agreement with past studies (
      • Bédard A.-C.V.
      • Trampush J.W.
      • Newcorn J.H.
      • Halperin J.M.
      Perceptual and motor inhibition in adolescents/young adults with childhood-diagnosed ADHD.
      ), it was also significantly lower in comorbid children relative to ADHD. Similar subadditive effects have been reported during a Go/NoGo paradigm, with reduced inhibitory control performance and atypical EEG activity in children with ADHD alone but not in ADHD with comorbid tics (
      • Morand-Beaulieu S.
      • Smith S.D.
      • Ibrahim K.
      • Wu J.
      • Leckman J.F.
      • Crowley M.J.
      • Sukhodolsky D.G.
      Electrophysiological signatures of inhibitory control in children with Tourette syndrome and attention-deficit/hyperactivity disorder.
      ). It is possible that these subadditive effects are related to compensatory or adaptive changes in neural organization thought to occur in children with PTD (
      • Jackson S.R.
      • Parkinson A.
      • Jung J.
      • Ryan S.E.
      • Morgan P.S.
      • Hollis C.
      • Jackson G.M.
      Compensatory Neural Reorganization in Tourette Syndrome.
      ), which may assist with certain cognitive functions in children with comorbid ADHD and PTD. Alternatively, this may also be explained by prior suggestions of some comorbid ADHD symptoms being driven by PTD-related issues (e.g., internal tic distraction) (
      • Morand-Beaulieu S.
      • Smith S.D.
      • Ibrahim K.
      • Wu J.
      • Leckman J.F.
      • Crowley M.J.
      • Sukhodolsky D.G.
      Electrophysiological signatures of inhibitory control in children with Tourette syndrome and attention-deficit/hyperactivity disorder.
      ,
      • Erenberg G.
      The Relationship Between Tourette Syndrome, Attention Deficit Hyperactivity Disorder, and Stimulant Medication: A Critical Review.
      ), thus affecting cognitive functioning differently than ADHD-driven issues.
      Aberrant connectivity followed a trend similar to the behavioral deficits observed, with the majority of atypical connections being associated with the presence of ADHD. Nodes of these connections comprise several frequently implicated networks in ADHD, including the default mode (DMN; precuneus, midtemporal gyrus), cognitive control (CCN; right superior frontal gyrus), and visual networks (VN; bilateral occipital cortex) (for meta-analysis, see Sutcubasi et al., 2020). Reduced DMN communication in pediatric ADHD is in agreement with some studies (
      • Fair D.A.
      • Posner J.
      • Nagel B.J.
      • Bathula D.
      • Dias T.G.C.
      • Mills K.L.
      • et al.
      Atypical Default Network Connectivity in Youth with Attention-Deficit/Hyperactivity Disorder.
      ,
      • Tomasi D.
      • Volkow N.D.
      Abnormal Functional Connectivity in Children with Attention-Deficit/Hyperactivity Disorder.
      ,
      • Zhang H.
      • Zhao Y.
      • Cao W.
      • Cui D.
      • Jiao Q.
      • Lu W.
      • et al.
      Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis.
      ), although hyperconnectivity has also been reported (
      • Barber A.D.
      • Jacobson L.A.
      • Wexler J.L.
      • Nebel M.B.
      • Caffo B.S.
      • Pekar J.J.
      • Mostofsky S.H.
      Connectivity supporting attention in children with attention deficit hyperactivity disorder.
      ). Lower connectivity among bilateral occipital areas has also similarly been noted in children with ADHD (
      • Zhang H.
      • Zhao Y.
      • Cao W.
      • Cui D.
      • Jiao Q.
      • Lu W.
      • et al.
      Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis.
      ). Significant negative correlations between the present connections and behavioral deficits provide support for a neurophysiological basis of behavioral deficits. In PTD, greater resting state information flow from the left postcentral cortex to right precuneus has been reported by prior studies noting increased sensorimotor network connectivity (
      • Worbe Y.
      • Malherbe C.
      • Hartmann A.
      • Pélégrini-Issac M.
      • Messé A.
      • Vidailhet M.
      • et al.
      Functional immaturity of cortico-basal ganglia networks in Gilles de la Tourette syndrome.
      ) as well as increased white matter tract density within the left postcentral cortex (
      • Thomalla G.
      • Siebner H.
      • Jonas M.
      • Bäumer T.
      • Biermann-Ruben K.
      • Hummel F.
      • et al.
      Structural changes in the somatosensory system correlate with tic severity in Gilles de la Tourette syndrome.
      ). This connection was not significantly correlated with any clinical behaviors, suggesting that greater information flow may instead represent overactive sensorimotor communication related to tic generation and execution. Lastly, lower connectivity from the left cuneus to right precuneus was observed in the three diagnostic groups relative to healthy controls. Atypical communication between these regions may be a common deficit among ADHD and PTD and could be a contributing factor in their comorbid diagnoses. It should be noted that while studies have indicated frontal regions to be a common area of deficits in bother disorders (
      • Castellanos F.X.
      • Sonuga-Barke E.J.S.
      • Milham M.P.
      • Tannock R.
      Characterizing cognition in ADHD: beyond executive dysfunction.
      ,
      • Yael D.
      • Vinner E.
      • Bar‐Gad I.
      Pathophysiology of tic disorders.
      ), only one atypical connection was noted here (right calcarine to right superior frontal) for ADHD-related deficits. This may be due to the analysis utilizing a factorial approach rather than 4-group approach, as well as the frequency nature of the underlying frontal causal activations (see limitations).
      No interactions between diagnoses were observed for connectivity measures, suggesting an additive model for the unique regional communication impairments present in each disorder. Similar additive effects of functional connectivity have been observed in frontoparietal and default mode networks in comorbid groups (
      • Openneer T.J.C.
      • Marsman J.-B.C.
      • van der Meer D.
      • Forde N.J.
      • Akkermans S.E.A.
      • Naaijen J.
      • et al.
      A graph theory study of resting-state functional connectivity in children with Tourette syndrome.
      ). It is possible that the additive/interactive nature of behavioral deficits may vary as a function of cognitive demand. For example, a prior selective attention study reported additive effects were seen primarily in spontaneous theta activity but interactive effects during event-related (post-stimulus) theta activity (
      • Yordanova J.
      • Heinrich H.
      • Kolev V.
      • Rothenberger A.
      Increased event-related theta activity as a psychophysiological marker of comorbidity in children with tics and attention-deficit/hyperactivity disorders.
      ). As suggested by the authors, resting state processes may tend towards additive models of disorder-independent deficits in regional activations and communication, while cognitive functions requiring greater resources may lead to more complex interactions of implicated networks. However, two studies utilizing cognitive control paradigms have reported additive effects on event-related potentials (ERPs) and event-related theta power when examining ADHD and PTD groups (
      • Shephard E.
      • Jackson G.M.
      • Groom M.J.
      The effects of co-occurring ADHD symptoms on electrophysiological correlates of cognitive control in young people with Tourette syndrome.
      ,
      • Morand-Beaulieu S.
      • Smith S.D.
      • Ibrahim K.
      • Wu J.
      • Leckman J.F.
      • Crowley M.J.
      • Sukhodolsky D.G.
      Electrophysiological signatures of inhibitory control in children with Tourette syndrome and attention-deficit/hyperactivity disorder.
      ). This may suggest that additive or interactive effects may be additionally dependent on the unique cognitive demands required, along with how these specific cognitive functions may be altered in each disorder. Further studies simultaneously evaluating both resting state and task-based connectivity may be beneficial for further understanding the full model of co-occurring ADHD and PTD and their unique interactions.
      Strengths of this study include a large sample and well-matched age and sex distributions by group. One potential limitation of this study is that tics were present among participants with PTD, which may cause instances of unique cognitive activity and transient noise in the resting state data. While noise was handled well using the data cleaning algorithms, event-related activity associated with tic expression may have contributed to the underlying resting-state connectivity measurements. A second limitation is that the statistical cluster-level correction implemented favors broadband spectral effects. It is possible that additional narrowband connectivity effects present amongst the diagnostic groups were not detected due to statistical methodology. Third, some subjects in the comorbid and PTD groups were taking prescribed medications, while those in the ADHD only group were not. While re-running the analyses using with medication status as a covariate did not alter any findings, future studies may strive to more stringently limit variations in medication status between study populations. A final limitation is the nature of pediatric studies, where children are undergoing dynamic developmental and disorder-related brain changes and adaptations. The present study, along with other pediatric studies, may not be fully comparable with adult populations where disorder and developmental states are more stabilized. For example, some prior longitudinal reports examining co-occurring ADHD/PTD have indicated transitions away from an additive model as development progresses towards adulthood (
      • Müller O.
      • Rothenberger A.
      • Brüni G.L.
      • Wang B.
      • Becker A.
      Questioning the long-term stability of the additive model in comorbid CTD+ADHD - The transition from childhood to adulthood.
      ). Additional longitudinal neurophysiological studies spanning childhood to adulthood may be beneficial in understanding how developmental trajectories for ADHD, PTD and comorbid ADHD+PTD change across development.
      In conclusion, the present study identified shared and unique patterns of atypical resting state functional connectivity with respect to diagnoses of ADHD and PTD. The majority of atypical connections were associated with the presence of ADHD, in concurrence with frequently reported ADHD-driven behavioral deficits. Similar to general behavioral deficits, aberrant resting state neural connectivity in pediatric ADHD and PTD combines additively in co-occurring cases. Significant associations between lower connectivity within DMN and CCN network nodes and ADHD-related behavior problems suggest neural network activity affects clinical impairment. The one unique PTD connectivity feature may be linked to tic generation and expression. Given the heterogeneity and frequent comorbidity of the two disorders, transdiagnostic studies can provide insight into the degree to which the disorders, individually and in combination, may contribute to impaired behavioral, cognitive, and neurophysiological impairments and warrant attention for treatment.

      Disclosures

      The authors have reported no biomedical financial interests or potential conflicts of interest.

      Acknowledgements

      We thank all families that participated in this research. Funding provided by National Institutes of Neurological Disease and Stroke grants 80160 and 97484 and National Institute of Mental Health grant R34 MH10182 (S.K.L.). The Swartz Center for Computational Neuroscience was founded in 2001 by a generous gift from founding donor Dr. Jerome Swartz of The Swartz Foundation (Old Field, New York).

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