Functional connectivity of specific brain networks related to social and communication dysfunction in adolescents with attention-deficit hyperactivity disorder

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Functional Connectivity of Specific Brain Networks Related to Social and Communication Dysfunction in Adolescents with Attention-Deficit Hyperactivity Disorder Mu-Hong Chen , Yen-Ling Chen , Ya-Mei Bai , Kai-Lin Huang , Hui-Ju Wu , Ju-Wei Hsu , Tung-Ping Su , Shih-Jen Tsai , Pei-Chi Tu , Cheng-Ta Li , Wei-Chen Lin , Yu-Te Wu PII: DOI: Reference:

S0165-1781(19)31574-4 https://doi.org/10.1016/j.psychres.2020.112785 PSY 112785

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Psychiatry Research

Received date: Revised date: Accepted date:

23 July 2019 11 January 2020 12 January 2020

Please cite this article as: Mu-Hong Chen , Yen-Ling Chen , Ya-Mei Bai , Kai-Lin Huang , Hui-Ju Wu , Ju-Wei Hsu , Tung-Ping Su , Shih-Jen Tsai , Pei-Chi Tu , Cheng-Ta Li , Wei-Chen Lin , Yu-Te Wu , Functional Connectivity of Specific Brain Networks Related to Social and Communication Dysfunction in Adolescents with Attention-Deficit Hyperactivity Disorder, Psychiatry Research (2020), doi: https://doi.org/10.1016/j.psychres.2020.112785

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Highlights 

Adolescents with ADHD exhibited higher total and subscale scores on SNAP-IV and Social Responsiveness Scale (SRS).

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Higher SNAP-IV and SRS scores were associated with functional dysconnectivity between the DMN, FPN and CON.

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Social cognition and communication impairment and ADHD may share a common functional dysconnection in the DMN, FPN, and CON.

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Functional Connectivity of Specific Brain Networks Related to Social and Communication Dysfunction in Adolescents with Attention-Deficit Hyperactivity Disorder

Mu-Hong Chen b,e,f,#, M.D., Yen-Ling Chen b,h,j,#, M.Sc., Ya-Mei Bai b,e,f,i, M.D., Ph.D., Kai-Lin Huang b,f, M.D., Hui-Ju Wu b, B.S.N., Ju-Wei Hsu b,f,*, M.D., Tung-Ping Su b,e,d,f,g, M.D., Shih-Jen Tsai b,e,f,i, M.D, PeiChi Tu a,b,c,d, M.D., Ph.D., Cheng-Ta Li b,e,f,i, M.D., Ph.D., Wei-Chen Lin b,e,f, M.D., Yu-Te Wu h,j,*, Ph.D.

a. Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan b. Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan c. Institute of Philosophy of Mind and Cognition, National Yang-Ming University, Taipei, Taiwan d. Division of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan e. Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan f. Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan g. Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan h. Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan i.

Brain Research Center, National Yang-Ming University, Taipei, Taiwan

j.

Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan 2

#: equally contributed.

Short title: Functional dysconnectivity in social function of ADHD Word counts: 2560; Table: 3; Figure: 2.

*: Corresponding authors Ju-Wei Hsu, M.D. E-mail: [email protected] Department of Psychiatry, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan.

Yu-Te Wu, Ph.D. Email: [email protected] Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No.155, Sec. 2, Linong St., Taipei, Taiwan

Conflict of Interest: No conflict of interest. Funding Source: The study was supported by grant from Taipei Veterans General Hospital (V106B-020, V107B-010, V107C-181) and Ministry of Science and Technology, Taiwan (107-2314-B-075-063-MY3, 108-2314-B-075 -037). The funding source had no role in any process of study.

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Financial Disclosure: All authors have no financial relationships relevant to this article to disclose. Acknowledgment: We thank Prof Wei-Tsuen Soong for his kindness to approve the use of Schedule for Affective Disorders and Schizophrenia for School-Age Children, Chinese version in our study. We thank Mr I-Fan Hu for his friendship and support.

Abstract Background: Adolescents with attention-deficit hyperactivity disorder (ADHD) may have impaired social cognition and communication. However, the functioning of the brain networks involved in the social cognition and communication impairment in ADHD patients remains unclear. Methods: In total, 18 adolescents with ADHD and 16 age- and sex-matched typically developing adolescents (controls)—all of whom underwent a brain magnetic resonance imaging examination—were enrolled. Their parents filled out Swanson, Nolan, and Pelham IV (SNAP-IV) and Social Responsiveness Scale (SRS) questionnaires. Functional connectivity analyses based on the default mode network, frontoparietal network, and cinguloopercular network were performed. Results: Compared with controls, adolescents with ADHD exhibited higher total and subscale scores on SNAP-IV and SRS. Higher SNAP-IV and SRS scores were associated with higher functional connectivity between the default mode network (ventromedial prefrontal cortex) and cinguloopercular network (anterior insula) and between the FPN (dorsolateral and prefrontal cortex) and cinguloopercular network, but with lower functional connectivity between the default mode network (posterior cingulate cortex) and 4

frontoparietal network (inferior parietal lobule) and between the default mode network (precuneus) and cinguloopercular network (temporoparietal junction). Discussion: Social cognition and communication impairment and ADHD may commonly share the aberrant functional connectivity in the default mode network, frontoparietal network, and cinguloopercular network. Keywords: Attention-deficit hyperactivity disorder; Social function; Magnetic resonance imaging; Functional connectivity.

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Attention-deficit hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder, affecting approximately 10% of children and adolescents in the United States and 7.5% of adolescents in Taiwan (Gau et al., 2005, Polanczyk et al., 2007, Rowland et al., 2015). The core ADHD symptoms, namely inattention, hyperactivity, impulsivity, and executive dysfunction, begin in childhood, and approximately 3050% of ADHD symptoms may persist into adulthood, leading to functional impairment in school, work, and social settings (Balint et al., 2008, Gau et al., 2005, Polanczyk et al., 2007, Rowland et al., 2015). ADHD etiology remains unclear. Multiple genes and noninherited factors, such as childhood adversity and chaotic family (i.e., Rutter’s Index of Adversity), may increase susceptibility to the disorder (Biederman et al., 2002, Thapar and Cooper, 2016).

Social cognition and communication impairment is common among children and adolescents with ADHD (Caillies et al., 2014, Mary et al., 2016, Stergiakouli et al., 2017). Mary et al. assessed the executive function, attention, and theory of mind among 31 children with ADHD and 31 typically developing children and found that the children with ADHD performed more poorly than did the controls in executive function, attention, and theory of mind tasks (Mary et al., 2016). The researchers suggested that inattention and executive dysfunction, particularly inhibition control, predicted the theory of mind impairment measured using the “Faux Pas” and “Reading the Mind in the Eyes” tasks (Mary et al., 2016). Caillies et al. investigated the relationship of executive function with theory of mind and irony comprehension among 15 6

children with ADHD and 15 age- and sex-matched controls and reported that the children with ADHD encountered problems with theory of mind and irony comprehension, particularly due to deficits in verbal reasoning function (Caillies et al., 2014). Shared genetic influences exist between social cognition and communication impairment and ADHD symptoms during development from childhood to late adolescence (Martin et al., 2014, Stergiakouli et al., 2017).

Two functional networks, the frontoparietal network (FPN) and cinguloopercular network (CON), potentially support the top–down control of executive functioning; these have therefore emerged as potential drivers of cognitive impairment and executive dysfunction in people with ADHD (Oldehinkel et al., 2016, Tao et al., 2017). Tao et al reported that the default mode network (DMN) exhibited decreased activity during goal-directed tasks and had a negative correlation with the FPN in spontaneous neural fluctuations in the absence of external stimuli (Tao et al., 2017). Accumulating evidence is indicating disrupted interactions among the DMN, FPN, and CON in ADHD (Tao et al., 2017). Tao et al. reported that patients with ADHD exhibited functional dysconnection within and between the DMN, FPN, and CON compared with controls (Tao et al., 2017). A meta-analysis of 55 functional magnetic resonance imaging (MRI) studies reported that relative to comparison subjects, hypoactivation in patients with ADHD was predominant in systems involved in executive function, namely the FPN and DMN (Cortese et al., 2012).

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Furthermore, the DMN, FPN, and CON may play crucial roles in social cognition and communication functioning (Abbott et al., 2016, de Lacy et al., 2017, Duan et al., 2017). De Lacy et al. reported that the coupling disruption among the DMN, FPN, and CON contributed to social cognition and communication impairment and that decreased functional connectivity between the right FPN and anterior DMN and increased functional connectivity between the CON and anterior DMN were related to social cognition and communication impairment (de Lacy et al., 2017). Duan et al. revealed that weaker connectivity within and between specific networks, such as the DMN and FPN, were correlated with poorer communication and social interaction skills (Duan et al., 2017). Based on the aforementioned clinical and imaging evidence, we hypothesized that aberrant functional connectivity among the FPN, CON, and DMN, which contributes to executive dysfunction, is crucial in the social cognition and communication impairment among adolescents with ADHD. Few studies have investigated this phenomenon in patients with ADHD.

By using resting-state functional MRI, we investigated the relationship of ADHD symptoms and social cognition and communication impairment with the functional connectivity of the DMN, FPN, and CON among adolescents with ADHD and typically developing controls. We re-assessed the hypothesis that the adolescents with ADHD are more likely to exhibit social cognition and communication impairment than are the typically developing controls and further suggested that ADHD symptoms and social cognition and communication impairment correlated with aberrant functional connectivity in the DMN, FPN, and CON. 8

Methods Subjects and clinical assessment. This study included adolescents aged 12–17 years diagnosed with ADHD by a board-certified child and adolescent psychiatrist based on a comprehensive diagnostic interview by using the Schedule for Affective Disorders and Schizophrenia for School-Age Children, Chinese version (Chen et al., 2017). Age- and sex-matched typically developing non-ADHD controls were recruited as the comparison group. Children with other psychiatric disorders, namely intellectual disability, autism spectrum disorder, schizophrenia, bipolar disorder, major depressive disorder, and alcohol and substance use disorders, or severe physical disorders, namely epilepsy, congenital anomalies, cerebrovascular diseases, and autoimmune diseases, were excluded. In total, 18 adolescents with ADHD (including 9 with combined type and 9 with inattentive type) and 16 age- and sex-matched controls were enrolled (13.56±1.72 vs. 14.06±1.53 years, p=.373). The questionnaires for the Swanson, Nolan, and Pelham IV (SNAP-IV) scale and Social Responsiveness Scale (SRS) were filled out by the adolescents’ parents (Chen et al., 2017, Wang et al., 2012). This study was approved by the Institutional Review Board of Taiwan Veterans General Hospital and the Department of Health of Taiwan. Written informed consent was obtained from the subjects and their parents. MRI image acquisition and Preprocessing. Whole-brain T2*-weighted gradient echo, echo-planar scans (repetition time [TR] = 2500 ms, echo time [TE] = 30 ms, flip angle [FA] = 90°, voxel size = 3.5 × 3.5 × 3.5 9

mm) were acquired using a 3.0-T GE MRI scanner in the Taipei Veterans General Hospital. The head motions of the subjects were restricted by a vacuum-beam pad in the scanner. 200 volumes of each subject were obtained with their eyes opened. During functional runs, the subject was instructed to remain awake with his or her eyes open (one run, each run 8 min and 20 s, 200 time points). The heads of the participants were supported using cushions, and all participants were provided earplugs (29 dB rating) for attenuating noise. A functional whole-brain image volume was composed of forty-three interleaved, axial slices, which were parallel to the inter-commissural plane. Anatomical whole-brain T1-weighted rapid acquisition gradient echo, sagittal magnetization prepared three-dimensional scans (MPRAGE sequence; TR = 2530 ms, TE = 3 ms, echo spacing = 7.25 ms, FA = 7°, field of view [FOV] = 256 × 256 mm, voxel size = 1 × 1 × 1 mm) were also obtained for better spatial registration. All images were processed using DPARSFA (http://rfmri.org/dpabi) and Statistical Parametric Mapping version 12 (Wellcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac.uk/spm) (Yan et al., 2016). Data were first corrected for slicedependent time shifts and then head motion through rigid-body affine of each volume to the first scan. Images were then spatially normalized into the Montreal Neurological Institute space using a nonlinear warping algorithm and subsequently spatially smoothed with a 6 mm3 full-width half-maximum Gaussian kernel. Spurious data were removed by band-pass filtering from .01 to .08 Hz, and by applying a regression model, containing the 24 parameters (Friston et al., 1996) obtained by realignment, the mean whole-brain signal, the mean signal from the lateral ventricles, and the mean signal within a deep white matter ROI. 10

Functional connectivity analyses. Functional connectivity analyses were conducted using MATLAB R2017b (Mathworks Inc, MA, USA) on a personal computer. The correlation map was computed first by extracting average time-series across all voxels in the Default Mode Network (DMN), the Fronto-Parietal Network (FPN), and the Cingulo-Opercular Network (CON) of the Dosenbach’s 160 functional regions of interest (Dosenbach et al., 2010) and then examined the correlation between each pair of these regions by using Pearson’s correlation coefficient. Since that we focused on DMN, FPN, and CON in the study, 87 nodes were chosen and 3,741 connections were created. The Fisher’s r-to-z transformation was conducted to normalize the correlation coefficients into z-scores. The between-group differences comparison in the z-transformed maps were implemented by independent samples t-test. Results were visualized using the BrainNet Viewer (https://www.nitrc.org/projects/bnv/) and an in-house MATLAB script. Furthermore, based on previous studies that the in-scanner head motion can induce spurious correlations or confound associations between functional connectivity and behavioral measures (Ciric et al., 2018, Power et al., 2012, Power et al., 2014, Siegel et al., 2014), we assessed the framewise displacement (FD) between cases and controls. Among case group, 6 patients with ADHD had mean FD >.2, and among control group only 1 subject had mean FD >.2 (p=.027). However, we did not exclude those with FD > .2 in the study analysis because of the small sample size. In order to avoid the spurious correlations of functional connectivity with behavioral measures, we performed the correlation analyses between clinical symptoms and functional connectivity that we found the difference between ADHD

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and control groups after controlling age, sex, and FD with multiple comparison corrections based on false discovery rate method. Age, sex, and FD entered in as regressors at the stage of nuisance regression. Clinical data analyses. Two sample t-test and Pearson’s chi-square tests were used to compare the continuous and categorical variables among groups, respectively. Correlation analysis of SNAP-4 and SRS was performed with the adjustment of age and sex. A two-tailed p value of less than .05 was considered statistically significant. All data processing and statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) Version 17 software (SPSS Inc.).

Results Among the adolescents with ADHD, half were being treated with methylphenidate (n = 8) or atomoxetine (n = 1; Table 1). Adolescents with ADHD exhibited higher total and subscale scores on the SNAP-IV scale (total [cohen’d, p-value: 1.91, <.001], inattention [1.57, <.001], hyperactivity [2.11, <.001], and opposition [1.30, .001]) and SRS (total [1.49, <.001], awareness [1.03, .005], cognition [1.84, <.001], communication [1.27, .001], motivation [.99, .008], mannerism [1.53, <.001]) than did the controls (Table 1). A correlation analysis revealed a positive correlation between the SNAP-IV and SRS scores. Higher inattention, hyperactivity, and opposition scores were associated with higher scores in awareness, cognition, communication, motivation, and mannerism (Table 2).

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Functional connectivity analyses based on the DMN, FPN, and CON (Table 3, Figure 1) indicated that compared with the controls, the adolescents with ADHD had higher functional connectivity within the DMN in the ventromedial prefrontal cortex (ventromedial PFC), between the DMN (ventromedial PFC, medial prefrontal cortex [medial PFC], fusiform cortex, and precuneus) and CON (ventral prefrontal cortex [ventral PFC], anterior insula, and superior temporal cortex), and between the FPN (ventrolateral prefrontal cortex [ventrolateral PFC] and dorsolateral prefrontal cortex [dorsolateral PFC]) and CON (ventral PFC and anterior insula). Furthermore, the adolescents with ADHD had lower functional connectivity within the DMN at precuneus, posterior cingulate cortex [PCC], intraparietal sulcus, and anterior prefrontal cortex; between the DMN (precuneus and PCC) and FPN (inferior parietal lobule); and between the DMN (precuneus) and CON (temporoparietal junction; Table 3, Figure 1).

Figure 2 illustrates the correlation between the functional connectivity and clinical variables (inattention, hyperactivity, opposition, awareness, cognition, communication, motivation, and mannerism). SNAP-IV and SRS scores were positively correlated with increased functional connectivity within the DMN in the ventromedial PFC, between the DMN (ventromedial PFC, medial PFC, and precuneus) and CON (ventral PFC, anterior insula, and temporal cortex), and between the FPN (dorsolateral PFC and ventrolateral PFC) and CON (anterior insula and ventral PFC), but negatively with decreased functional connectivity within the DMN in the precuneus and PCC and between the DMN (precuneus) and FPN (inferior parietal lobule). 13

Discussion The results re-confirmed the past results that the adolescents with ADHD exhibited more social cognition and communication impairment symptoms than did the controls and that the core ADHD symptoms, namely inattention, hyperactivity, and impulsivity, were positively correlated with the social cognition and communication impairment symptoms, namely those related to social awareness, cognition, communication, and motivation. Furthermore, the adolescents with ADHD exhibited aberrant functional connectivity within and between the DMN, FPN, and CON compared with the controls. Increased functional connectivity between the DMN (ventromedial PFC) and CON (anterior insula) and between the FPN (dorsolateral PFC) and CON (anterior insula) and decreased functional connectivity between the DMN (PCC) and FPN (inferior parietal lobule) and between the DMN (precuneus) and CON (temporoparietal junction) were associated with higher SNAP-IV scale scores (more ADHD symptoms) and higher SRS scores.

The major finding of this study was that the alteration of functional connectivity within the DMN in the ventromedial PFC and between the DMN (ventromedial PFC and medial PFC) and CON (ventral PFC and anterior insula) is accompanied by increased ADHD and social cognition and communication impairment. Sonuga-Barke, Castellanos and colleagues proposed the “default-mode interference” hypothesis, and suggested that variability in performance in ADHD may be due to a dysfunctional synchronization in the DMN or altered 14

interactions between DMN and task positive networks, such as CON and FPN (Castellanos et al., 2006, Mills et al., 2018, Sonuga-Barke and Castellanos, 2007). The ventromedial PFC and medial PFC are parts of the DMN and have been implicated in numerous social, cognitive, and affective functions (Myers-Schulz and Koenigs, 2012). Moreover, the ventral PFC of the CON contributes to behavioral top–down inhibition, emotional regulation, and social flexibility (Nelson and Guyer, 2011), and the anterior insula, involved in cognitive and emotional processes, is crucial in attentional and behavioral regulation and interoceptive and emotional awareness (Menon and Uddin, 2010). Fassbender et al. reported that increased functional connectivity and a lack of suppression within the DMN, including the ventromedial PFC, is linked to increased distractibility in ADHD patients (Fassbender et al., 2009). Mowinckel et al assessed the functional connectivity in 20 patients with ADHD on and off methylphenidate while performing a decision-making task, and demonstrated that a pattern of increased coupling between DMN and the ventral attention network (i.e., ventral frontal cortex) was evident when patients were unmedicated compared with controls, and also associated with reduced task performance across groups, indicating they are general network changes correlated with reduced performance (Mowinckel et al., 2017). Von Rhein et al. found that ADHD in adolescents and young adults is associated with increased neural responses in the frontostriatal circuitry, including the ventral PFC and anterior frontal cortex, from reward anticipation and receipt (von Rhein et al., 2015). Yang et al. reported a positive correlation between ADHD symptoms and functional connectivity between the medial PFC and insular cortex and between the dorsolateral PFC and insular cortex, which was normalized using methylphenidate (Yang et 15

al., 2016). Scofield et al investigated the temporal dynamics of brain network changes for ADHD in restingstate fMRI, and found that typical dynamic state switching may be altered in children with ADHD, showing greater variability than controls that in turn might be specific to regions within the DMN (Scofield et al., 2019). Increased functional connectivity of the CON in association with the DMN during rest is considered a core neurocharacteristic of ADHD (Hoekzema et al., 2014). Furthermore, a meta-analysis showed that distinct dysfunction of ventromedial PFC-based circuits corresponded with executive dysfunction and social cognition problems (i.e., facial emotion recognition, theory of mind ability, and self-relevant information processing) at a psychopathological level and with ADHD, depression, and social anxiety at a diagnostic level (Hiser and Koenigs, 2018). Accumulating evidence is suggesting that aberrant functional connectivity involving the ventral PFC and insular cortex is related to social cognition and communication impairment and autistic tendency (Odriozola et al., 2016, Rane et al., 2015, Yamada et al., 2016). In the present study, we found that ADHD and social symptoms (i.e., those in cognition, awareness, and motivation) shared the pattern of increased functional connectivity within the DMN and between the DMN and CON, which were compatible with aforementioned findings of default-mode interference hypothesis.

Furthermore, increased functional connectivity between the FPN (ventrolateral PFC and dorsolateral PFC) and CON (ventral PFC and anterior insula) was observed in the adolescents with ADHD; it was positively correlated with greater SNAP-IV and SRS scores, indicating that the dysconnection of the FPN and CON 16

contributes to attention and social problems. In addition to accounting for the top–down control of executive functioning, FPN and CON are involved in social awareness and cognition (de Lacy et al., 2017, Duan et al., 2017, Oldehinkel et al., 2016, Tao et al., 2017). Hoekzema et al. reported that increased and insufficient suppression of the functional connectivity in dorsolateral PFC-related networks during rest was an ADHD biomarker (Hoekzema et al., 2014).

We also found that adolescents with ADHD exhibited decreased functional connectivity within the DMN in the precuneus and PCC, between the DMN (precuneus and PCC) and FPN (inferior parietal lobule), and between the DMN (precuneus) and CON (temporoparietal junction) compared with the controls. Moreover, a negative correlation was noted between decreased functional connectivity within the DMN (precuneus and PCC) and between the DMN (precuneus and PCC) and FPN (inferior parietal lobule) with ADHD and social cognition and communication impairment. Castellanos et al. reported an extensive ADHD-related reduction in functional connectivity between the precuneus and other DMN components (PCC) (Castellanos et al., 2008). Tomasi et al. found that children with ADHD exhibited lower connectivity between the DMN (precuneus) and dorsal attention network regions (superior parietal cortex) compared with controls (Tomasi and Volkow, 2012). Bonnelle et al. demonstrated that the decreased connectivity of the precuneus with the rest of the DMN predicted the impairment of sustained attention after traumatic brain injury, which suggests that abnormalities in DMN function are sensitive markers of attention and cognitive deficits (Bonnelle et al., 2011). Mills et 17

reported that children with ADHD had decreased negative connectivity between DMN and task positive networks, and further found that connectivity continued to become more negative between these networks throughout development (7–15 years of age) in children with ADHD (Mills et al., 2018). Furthermore, studies have examined the potential roles of dysconnectivity within the DMN in the PCC and precuneus and between the DMN and CON or FPN (temporoparietal junction and PFC) in the social cognition and communication impairment involved in mentalizing both self and others (Lombardo et al., 2010, Weng et al., 2010). A review reported that joint attention development, critical for social awareness, cognition, and communication, is commonly impaired in patients with ADHD and is prominently involved in the functional connectivity of the DMN, FPN, and CON (Carpenter Rich et al., 2009, Mundy, 2018). Here, we found that ADHD and social responsiveness symptoms shared a common decrease in functional connectivity within the DMN and between the DMN and FPN, suggesting a synergic interaction of attention with social cognition and communication impairment.

Based on our findings, we revealed that attention and social cognition symptoms of ADHD were positively correlated to the hyperconnectivity within the anterior part of DMN (ventromedial PFC) and between both the anterior (i.e., ventromedial PFC) and posterior (i.e., precuneus) parts of DMN and CON and negatively associated with the hypoconnectivity within the posterior part of DMN (precuneus, PCC) and between the posterior (i.e., precuneus) part of DMN and FPN. Previous evidence has suggested that the human brain 18

developed from the posterior parts, including parietal and occipital lobes, forwardly to the frontal lobe, especially prefrontal cortex, and laterally to the temporal lobe (Gogtay et al., 2004). In our study, the hypoconnectivity involving the posterior part of brain, such as precuneus, may indicate the delayed neurodevelopment of ADHD, and the hyperconnectivity involving the anterior part of brain, particularly PFC, may imply the compensatory effect of ADHD brain functioning. Both the positive and negative correlation of attention and social impairment symptoms with those functional connectivity may suggest the shared functional connectivity alterations in the attention and social impairment symptoms of ADHD.

The study has several limitations. First, both medicated and nonmedicated adolescents with ADHD were enrolled and analyzed because the parents of the medicated patients were concerned about the consequences of discontinuing treatment. Our study design was ethically more appropriate for patients who received regular treatment, and it could provide more naturalistic data. Additional studies elucidating the social cognition and related brain network functioning in drug-naïve or nonmedicated adolescents with ADHD is required. Second, we focused on the social cognition and communication impairment and related functional connectivity impairment in adolescents with ADHD. Whether our findings could be generalized to children or adults with ADHD requires further investigation.

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In conclusion, the adolescents with ADHD were more likely to exhibit social cognition and communication impairment than were the controls. Core ADHD symptoms, namely inattention, hyperactivity, and impulsivity, may be responsible for the occurrence of social cognition and communication impairment. Moreover, ADHD and social cognition and communication impairment may commonly share the aberrant functional connectivity, such as increased connectivity within the DMN, between the DMN and CON, and between FPN and CON and reduced connectivity between the DMN and FPN or CON. Our results suggest shared neuropathology between ADHD and social cognition and communication impairment, which requires further investigation.

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References Abbott, A. E., Nair, A., Keown, C. L., Datko, M., Jahedi, A., Fishman, I. & Muller, R. A., 2016. Patterns of Atypical Functional Connectivity and Behavioral Links in Autism Differ Between Default, Salience, and Executive Networks. Cereb Cortex 26, 4034-45. Balint, S., Czobor, P., Meszaros, A., Simon, V. & Bitter, I., 2008. Neuropsychological impairments in adult attention deficit hyperactivity disorder: a literature review. Psychiatr Hung 23, 324-35. Biederman, J., Faraone, S. V. & Monuteaux, M. C., 2002. Differential effect of environmental adversity by gender: Rutter's index of adversity in a group of boys and girls with and without ADHD. Am J Psychiatry 159, 1556-62. Bonnelle, V., Leech, R., Kinnunen, K. M., Ham, T. E., Beckmann, C. F., De Boissezon, X., Greenwood, R. J. & Sharp, D. J., 2011. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J Neurosci 31, 13442-51. Caillies, S., Bertot, V., Motte, J., Raynaud, C. & Abely, M., 2014. Social cognition in ADHD: irony understanding and recursive theory of mind. Res Dev Disabil 35, 3191-8. Carpenter Rich, E., Loo, S. K., Yang, M., Dang, J. & Smalley, S. L., 2009. Social functioning difficulties in ADHD: association with PDD risk. Clin Child Psychol Psychiatry 14, 329-44. Castellanos, F. X., Margulies, D. S., Kelly, C., Uddin, L. Q., Ghaffari, M., Kirsch, A., Shaw, D., Shehzad, Z., Di Martino, A., Biswal, B., Sonuga-Barke, E. J., Rotrosen, J., Adler, L. A. & Milham, M. P., 2008. 21

Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biol Psychiatry 63, 332-7. Castellanos, F. X., Sonuga-Barke, E. J., Milham, M. P. & Tannock, R., 2006. Characterizing cognition in ADHD: beyond executive dysfunction. Trends Cogn Sci 10, 117-23. Chen, Y. L., Shen, L. J. & Gau, S. S., 2017. The Mandarin version of the Kiddie-Schedule for Affective Disorders and Schizophrenia-Epidemiological version for DSM-5 - A psychometric study. J Formos Med Assoc 116, 671-678. Ciric, R., Rosen, A. F. G., Erus, G., Cieslak, M., Adebimpe, A., Cook, P. A., Bassett, D. S., Davatzikos, C., Wolf, D. H. & Satterthwaite, T. D., 2018. Mitigating head motion artifact in functional connectivity MRI. Nat Protoc 13, 2801-2826. Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M. P. & Castellanos, F. X., 2012. Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. Am J Psychiatry 169, 1038-55. de Lacy, N., Doherty, D., King, B. H., Rachakonda, S. & Calhoun, V. D., 2017. Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum. Neuroimage Clin 15, 513-524. Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., Barnes, K. A., Dubis, J. W., Feczko, E., Coalson, R. S., Pruett, J. R., Jr., Barch, D. M., Petersen, S. E. & Schlaggar, B. L., 2010. Prediction of individual brain maturity using 22

fMRI. Science 329, 1358-61. Duan, X., Chen, H., He, C., Long, Z., Guo, X., Zhou, Y. & Uddin, L. Q., 2017. Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism. Prog Neuropsychopharmacol Biol Psychiatry 79, 434-441. Fassbender, C., Zhang, H., Buzy, W. M., Cortes, C. R., Mizuiri, D., Beckett, L. & Schweitzer, J. B., 2009. A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res 1273, 114-28. Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. & Turner, R., 1996. Movement-related effects in fMRI time-series. Magn Reson Med 35, 346-55. Gau, S. S., Chong, M. Y., Chen, T. H. & Cheng, A. T., 2005. A 3-year panel study of mental disorders among adolescents in Taiwan. Am J Psychiatry 162, 1344-50. Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent, T. F., 3rd, Herman, D. H., Clasen, L. S., Toga, A. W., Rapoport, J. L. & Thompson, P. M., 2004. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A 101, 8174-9. Hiser, J. & Koenigs, M., 2018. The Multifaceted Role of the Ventromedial Prefrontal Cortex in Emotion, Decision Making, Social Cognition, and Psychopathology. Biol Psychiatry 83, 638-647. Hoekzema, E., Carmona, S., Ramos-Quiroga, J. A., Richarte Fernandez, V., Bosch, R., Soliva, J. C., Rovira, M., Bulbena, A., Tobena, A., Casas, M. & Vilarroya, O., 2014. An independent components and functional 23

connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD. Hum Brain Mapp 35, 1261-72. Lombardo, M. V., Chakrabarti, B., Bullmore, E. T., Wheelwright, S. J., Sadek, S. A., Suckling, J., Consortium, M. A. & Baron-Cohen, S., 2010. Shared neural circuits for mentalizing about the self and others. J Cogn Neurosci 22, 1623-35. Martin, J., Hamshere, M. L., Stergiakouli, E., O'Donovan, M. C. & Thapar, A., 2014. Genetic risk for attention-deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population. Biol Psychiatry 76, 664-71. Mary, A., Slama, H., Mousty, P., Massat, I., Capiau, T., Drabs, V. & Peigneux, P., 2016. Executive and attentional contributions to Theory of Mind deficit in attention deficit/hyperactivity disorder (ADHD). Child Neuropsychol 22, 345-65. Menon, V. & Uddin, L. Q., 2010. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 214, 655-67. Mills, B. D., Miranda-Dominguez, O., Mills, K. L., Earl, E., Cordova, M., Painter, J., Karalunas, S. L., Nigg, J. T. & Fair, D. A., 2018. ADHD and attentional control: Impaired segregation of task positive and task negative brain networks. Netw Neurosci 2, 200-217. Mowinckel, A. M., Alnaes, D., Pedersen, M. L., Ziegler, S., Fredriksen, M., Kaufmann, T., Sonuga-Barke, E., Endestad, T., Westlye, L. T. & Biele, G., 2017. Increased default-mode variability is related to reduced 24

task-performance and is evident in adults with ADHD. Neuroimage Clin 16, 369-382. Mundy, P., 2018. A review of joint attention and social-cognitive brain systems in typical development and autism spectrum disorder. Eur J Neurosci 47, 497-514. Myers-Schulz, B. & Koenigs, M., 2012. Functional anatomy of ventromedial prefrontal cortex: implications for mood and anxiety disorders. Mol Psychiatry 17, 132-41. Nelson, E. E. & Guyer, A. E., 2011. The development of the ventral prefrontal cortex and social flexibility. Dev Cogn Neurosci 1, 233-45. Odriozola, P., Uddin, L. Q., Lynch, C. J., Kochalka, J., Chen, T. & Menon, V., 2016. Insula response and connectivity during social and non-social attention in children with autism. Soc Cogn Affect Neurosci 11, 433-44. Oldehinkel, M., Beckmann, C. F., Franke, B., Hartman, C. A., Hoekstra, P. J., Oosterlaan, J., Heslenfeld, D., Buitelaar, J. K. & Mennes, M., 2016. Functional connectivity in cortico-subcortical brain networks underlying reward processing in attention-deficit/hyperactivity disorder. Neuroimage Clin 12, 796-805. Polanczyk, G., de Lima, M. S., Horta, B. L., Biederman, J. & Rohde, L. A., 2007. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 164, 942-8. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E., 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142-54. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E., 2014. Methods to 25

detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320-41. Rane, P., Cochran, D., Hodge, S. M., Haselgrove, C., Kennedy, D. N. & Frazier, J. A., 2015. Connectivity in Autism: A Review of MRI Connectivity Studies. Harv Rev Psychiatry 23, 223-44. Rowland, A. S., Skipper, B. J., Umbach, D. M., Rabiner, D. L., Campbell, R. A., Naftel, A. J. & Sandler, D. P., 2015. The Prevalence of ADHD in a Population-Based Sample. J Atten Disord 19, 741-54. Scofield, J. E., Johnson, J. D., Wood, P. K. & Geary, D. C., 2019. Latent resting-state network dynamics in boys and girls with attention-deficit/hyperactivity disorder. PLoS One 14, e0218891. Siegel, J. S., Power, J. D., Dubis, J. W., Vogel, A. C., Church, J. A., Schlaggar, B. L. & Petersen, S. E., 2014. Statistical improvements in functional magnetic resonance imaging analyses produced by censoring highmotion data points. Hum Brain Mapp 35, 1981-96. Sonuga-Barke, E. J. & Castellanos, F. X., 2007. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neurosci Biobehav Rev 31, 977-86. Stergiakouli, E., Davey Smith, G., Martin, J., Skuse, D. H., Viechtbauer, W., Ring, S. M., Ronald, A., Evans, D. E., Fisher, S. E., Thapar, A. & St Pourcain, B., 2017. Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development. Mol Autism 8, 18. Tao, J., Jiang, X., Wang, X., Liu, H., Qian, A., Yang, C., Chen, H., Li, J., Ye, Q., Wang, J. & Wang, M., 2017. Disrupted Control-Related Functional Brain Networks in Drug-Naive Children with AttentionDeficit/Hyperactivity Disorder. Front Psychiatry 8, 246. 26

Thapar, A. & Cooper, M., 2016. Attention deficit hyperactivity disorder. Lancet 387, 1240-50. Tomasi, D. & Volkow, N. D., 2012. Abnormal functional connectivity in children with attentiondeficit/hyperactivity disorder. Biol Psychiatry 71, 443-50. von Rhein, D., Cools, R., Zwiers, M. P., van der Schaaf, M., Franke, B., Luman, M., Oosterlaan, J., Heslenfeld, D. J., Hoekstra, P. J., Hartman, C. A., Faraone, S. V., van Rooij, D., van Dongen, E. V., Lojowska, M., Mennes, M. & Buitelaar, J., 2015. Increased neural responses to reward in adolescents and young adults with attention-deficit/hyperactivity disorder and their unaffected siblings. J Am Acad Child Adolesc Psychiatry 54, 394-402. Wang, J., Lee, L. C., Chen, Y. S. & Hsu, J. W., 2012. Assessing autistic traits in a Taiwan preschool population: cross-cultural validation of the Social Responsiveness Scale (SRS). J Autism Dev Disord 42, 2450-9. Weng, S. J., Wiggins, J. L., Peltier, S. J., Carrasco, M., Risi, S., Lord, C. & Monk, C. S., 2010. Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Res 1313, 202-14. Yamada, T., Itahashi, T., Nakamura, M., Watanabe, H., Kuroda, M., Ohta, H., Kanai, C., Kato, N. & Hashimoto, R. I., 2016. Altered functional organization within the insular cortex in adult males with highfunctioning autism spectrum disorder: evidence from connectivity-based parcellation. Mol Autism 7, 41. Yan, C. G., Wang, X. D., Zuo, X. N. & Zang, Y. F., 2016. DPABI: Data Processing & Analysis for (Resting27

State) Brain Imaging. Neuroinformatics 14, 339-51. Yang, Z., Kelly, C., Castellanos, F. X., Leon, T., Milham, M. P. & Adler, L. A., 2016. Neural Correlates of Symptom Improvement Following Stimulant Treatment in Adults with Attention-Deficit/Hyperactivity Disorder. J Child Adolesc Psychopharmacol 26, 527-36.

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Table 1. Demographic and clinical data among adolescents with ADHD and controls*

Age (years, SD) Male (n, %)

Adolescents with

Controls

Effect size

p-value

ADHD (n=18)

(n=16)

(Cohen’d)

13.56 (1.72)

14.06 (1.53)

.373

13 (72.2)

8 (50%)

.291

Subtypes of ADHD (n, %) Inattentive type

9 (5.0)

Combined type

9 (5.0)

ADHD medications (n, %) Methylphenidate

8 (44.4)

Atomoxetine

1 (5.6)

SNAP-4 (mean, SD)

32.72 (14.16)

11.25 (7.24)

1.91

<.001

Inattention

14.33 (5.95)

6.00 (4.58)

1.57

<.001

Hyperactivity

8.89 (4.73)

1.44 (1.63)

2.11

<.001

Opposition

9.50 (5.67)

3.81 (2.54)

1.30

.001

73.39 (33.00)

33.38 (18.61)

1.49

<.001

Awareness

1.33 (3.09)

6.75 (3.84)

1.03

.005

Cognition

15.33 (6.32)

5.50 (4.16)

1.84

<.001

Communication

24.11 (12.96)

11.13 (6.35)

1.27

.001

Motivation

11.67 (6.15)

6.56 (3.92)

.99

.008

Mannerism

11.94 (7.30)

3.44 (2.87)

1.53

<.001

SRS (mean, SD)

SD: standard deviation; SNAP-4: Chinese version of the Swanson, Nolan and Pehlam version IV; SRS: Social Responsiveness Scale. *Two sample t-test and Pearson’s chi-square tests were used to compare the continuous and categorical variables among groups, respectively.

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Table 2. Correlation of SNAP-4 and SRS.

SNAP-4

SRS, R (p-value) Awareness

Cognition

Communication

Motivation

Mannerism

Total

Inattention

.565 (.001)

.757 (<.001)

.691 (<.001)

.682 (<.001)

.720 (<.001)

.746 (<.001)

Hyperactivity

.470 (.007)

.741 (<.001)

.665 (<.001)

.481 (.005)

.730 (<.001)

.691 (<.001)

Oppositional

.383 (.030)

.636 (<.001)

.543 (.001)

.440 (.012)

.641 (<.001)

.588 (<.001)

Total

.528 (.002)

.786 (<.001)

.701 (<.001)

.602 (<.001)

.768 (<.001)

.749 (<.001)

SNAP-4: Chinese version of the Swanson, Nolan and Pelham version IV; SRS: Social Responsiveness Scale.

Table 3. The within-network and between-network functional connectivity in the patients with ADHD compared with the controls Region1

MNI

Region2

MNI

coordinates

ADHD

Control

p-value

coordinates

Network Connectivity

ADHD > Control Within network connectivity vmPFC

6

64

3

vmPFC

8

42

-5

.21027

-.03967

.00400

DMN

vmPFC

9

51

16

vmPFC

8

42

-5

.36726

.07615

.00444

DMN

Between network connectivity vmPFC

-6

50

-1

vPFC

34

32

7

.31145

.08140

.00193

DMN-CON

vmPFC

-11

45

17

vPFC

34

32

7

.21461

.00242

.00042

DMN-CON

Fusiform

28

-37

-15

vPFC

34

32

7

.17832

-.02590

.00203

DMN-CON

Occipital

-28

-42

-11

vPFC

34

32

7

.14747

-.02800

.00427

DMN-CON

vlPFC

39

42

16

vPFC

34

32

7

.25050

.04850

.00129

FPN-CON

mPFC

0

51

32

aI

38

21

-1

.07369

-.14535

.00277

DMN-CON

vmPFC

-11

45

17

aI

38

21

-1

.16404

-.03931

.00344

DMN-CON

dlPFC

-44

27

33

aI

38

21

-1

.04960

-.13724

.00416

FPN-CON

IPS

32

-59

41

aI

38

21

-1

-.01628

-.20772

.00478

DMN-CON

mPFC

0

51

32

aI

-36

18

2

.03145

-.16649

.00435

DMN-CON

IPS

-36

-69

40

aI

-36

18

2

.01544

-.21432

.00343

DMN-CON

dlPFC

46

28

31

aI

-36

18

2

.07448

-.09817

.00430

FPN-CON

sFrontal

-16

29

54

sTemporal

42

-46

21

.19112

-.00740

.00465

DMN-CON

Precuneus

5

-50

33

Temporal

-59

-47

11

.40794

.18303

.00347

DMN-CON

ADHD < Control Within network connectivity Precuneus

9

-43

25

PCC

-11

-58

17

-.07458

.16164

.00168

DMN

Precuneus

9

-43

25

IPS

-36

-69

40

.34875

.61041

.00299

DMN

aPFC

-25

51

27

Occipital

-2

-75

32

.00657

.20080

.00203

DMN

30

Between network connectivity Precuneus

9

-43

25

IPL

-41

-40

42

-.09118

.12565

.00067

DMN-FPN

PCC

-5

-43

25

IPL

44

-52

47

.83427

1.08648

.00279

DMN-FPN

Precuneus

9

-43

25

TPJ

-52

-63

15

.28704

.59364

.00201

DMN-CON

DMN: Default Mode Network; FPN: Fronto-Parietal Network; CON: Cingulo-Opercular Network; vmPFC: ventral medial prefrontal cortex; vPFC: ventral prefrontal cortex; vlPFC: ventral lateral prefrontal cortex; mPFC: medial prefrontal cortex; aI: anterior insula; dlPFC: dorsal lateral prefrontal cortex; IPS: intraparietal sulcus; sFrontal: superior frontal cortex; sTemporal: superior temporal cortex; PCC: posterior cingulate cortex; IPS: intraparietal sulcus; aPFC: anterior prefrontal cortex; IPL: inferior parietal lobule; TPJ: temporal parietal junction

Figure 1. 1.1. Increased connectivity within and between the DMN (blue nodes), the FPN (green nodes), and the CON (red nodes) in patients with ADHD compared with controls. 1.2. Decreased connectivity within and between the DMN (blue nodes), the FPN (green nodes), and the CON (red nodes) in patients with ADHD compared with controls. (a: Lateral view of left hemisphere; b: Lateral view of right hemisphere; c: Dorsal view; d: Ventral view).

31

Figure 2. Correlation of functional connectivity and clinical variables, adjusting for age, sex, and framewise displacement *.

vmPFC: ventral medial prefrontal cortex; vPFC: ventral prefrontal cortex; vlPFC: ventral lateral prefrontal cortex; mPFC: medial prefrontal cortex; aI: anterior insula; dlPFC: dorsal lateral prefrontal cortex; IPS: intraparietal sulcus; sFrontal: superior frontal cortex; sTemporal: superior temporal cortex; PCC: posterior cingulate cortex; IPS: intraparietal sulcus; aPFC: anterior prefrontal cortex; IPL: inferior parietal lobule; TPJ: temporal parietal junction; SNAP-4: Chinese version of the Swanson, Nolan and Pehlam version IV; SRS: Social Responsiveness Scale.

32