Anxiety disorders, including generalized anxiety disorder (GAD), phobia, and panic disorder, as well as related obsessive–compulsive disorder (OCD), are among the most common comorbid conditions among individuals with autism spectrum disorder (ASD) (Lai et al., Reference Lai, Kassee, Besney, Bonato, Hull, Mandy and Ameis2019). They disproportionately affect individuals with ASD, with an estimated prevalence of 20% (anxiety) and 9% (OCD) (Lai et al., Reference Lai, Kassee, Besney, Bonato, Hull, Mandy and Ameis2019) compared with prevalence in the wider population of 6% (anxiety) (Polanczyk, Salum, Sugaya, Caye, & Rohde, Reference Polanczyk, Salum, Sugaya, Caye and Rohde2015) and <1% (OCD) (Adam, Meinlschmidt, Gloster, & Lieb, Reference Adam, Meinlschmidt, Gloster and Lieb2012). Increased anxiety in ASD is also associated with more severe restricted, repetitive behaviors (RRBs) (Cashin & Yorke, Reference Cashin and Yorke2018; Gotham et al., Reference Gotham, Bishop, Hus, Huerta, Lund, Buja and Lord2013; Rodgers, Glod, Connolly, & McConachie, Reference Rodgers, Glod, Connolly and McConachie2012) and social–communication abnormalities (Duvekot, Ende, Verhulst, & Greaves-Lord, Reference Duvekot, Ende, Verhulst and Greaves-Lord2018). However, comorbid anxiety in ASD is relatively understudied, due to challenges in objectively evaluating anxiety among patients and the atypical nature of the clinical expression of anxiety in some individuals with ASD, including unusual phobias (e.g., toilets), behavioral distress in anticipation of change in routines or the environment, and frequent sensory fears (e.g., loud sounds). Treatments aimed at mitigating anxiety-related issues in ASD also appear to be less effective than in non-ASD populations, highlighting the need to better understand neurobiological mechanisms of anxiety in ASD (King et al., Reference King, Hollander, Sikich, McCracken, Scahill, Bregman and Ritz2009; Selles et al., Reference Selles, Arnold, Phares, Lewin, Murphy and Storch2015).
Resting-state functional magnetic resonance imaging (rs-fMRI) studies are well suited to objectively investigating neurophysiological processes associated with anxiety in ASD. ASD appears to be a disorder involving atypical brain connectivity (Kessler, Seymour, & Rippon, Reference Kessler, Seymour and Rippon2016; Müller, Reference Müller2007), and functional connectivity of rs-fMRI has been used in ASD to predict clinical outcomes using cross-validated prediction models (Abraham et al., Reference Abraham, Milham, Di Martino, Craddock, Samaras, Thirion and Varoquaux2017; Plitt, Barnes, Wallace, Kenworthy, & Martin, Reference Plitt, Barnes, Wallace, Kenworthy and Martin2015). The task-free nature of rs-fMRI also has advantages for quantifying the neurobiology of anxiety disorders, which are characterized by idiosyncratic responses to anxiety-related cues that can present challenges for task-based fMRI.
Findings from functional connectivity studies in ASD have been notably inconsistent (Müller et al., Reference Müller, Shih, Keehn, Deyoe, Leyden and Shukla2011; Picci, Gotts, & Scherf, Reference Picci, Gotts and Scherf2016), probably due to methodological and analytical variations (Müller et al., Reference Müller, Shih, Keehn, Deyoe, Leyden and Shukla2011; Nair et al., Reference Nair, Keown, Datko, Shih, Keehn and Müller2014) as well as clinical and neurobiological heterogeneity across affected individuals (King et al., Reference King, Prigge, King, Morgan, Weathersby, Fox and Anderson2019; Linke, Olson, Gao, Fishman, & Müller, Reference Linke, Olson, Gao, Fishman and Müller2017; Uddin, Supekar, & Menon, Reference Uddin, Supekar and Menon2013). The prevalence of comorbid conditions in ASD, including anxiety, OCD, mood disorders, attention-deficit/hyperactivity disorder (ADHD), conduct disorders, and schizophrenia (Lai et al., Reference Lai, Kassee, Besney, Bonato, Hull, Mandy and Ameis2019) likely represents a substantial source of neurobiological variability, though few studies have systematically compared brain connectivity between ASD individuals with and without comorbid psychiatric disorders. The advent of multi-site ASD imaging databases with large sample sizes, including the EU-AIMS Longitudinal European Autism Project (LEAP) and the Autism Brain Imaging Data Exchange (ABIDE) (Di Martino et al., Reference Di Martino, Yan, Li, Denio, Castellanos, Alaerts and Milham2014, Reference Di Martino, O'Connor, Chen, Alaerts, Anderson, Assaf and Milham2017), presents an opportunity to separate distinct subgroups of individuals with ASD based on comorbid conditions, helping to resolve inconsistencies across studies of the neurobiology of ASD.
Preclinical and clinical studies of anxiety disorders have implicated dysfunction of the amygdala. The amygdala has dense bidirectional connections with the anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC), (Kim et al., Reference Kim, Loucks, Palmer, Brown, Solomon, Marchante and Whalen2011) and heightened amygdala reactivity in patients with anxiety disorders has been shown to be associated with diminished top-down ACC/mPFC control of amygdala reactivity (Etkin, Reference Etkin, Stein and Steckler2009; Jalbrzikowski et al., Reference Jalbrzikowski, Larsen, Hallquist, Foran, Calabro and Luna2017; Qin et al., Reference Qin, Young, Duan, Chen, Supekar and Menon2014; Swartz, Carrasco, Wiggins, Thomason, & Monk, Reference Swartz, Carrasco, Wiggins, Thomason and Monk2014). ACC/mPFC targets of amygdala nuclei include dorsal and ventral divisions that serve distinct functions related to anxiety derived from their roles in emotion processing (Etkin, Reference Etkin, Stein and Steckler2009). Emotion processing involves monitoring one's internal emotional state and modifying the emotion being expressed to regulate one's response based on context. The dorsal division, including the dorsal anterior cingulate cortex (dACC) and the adjacent dorsomedial prefrontal cortex (dmPFC), is involved in emotion monitoring and evaluation (Etkin, Reference Etkin, Stein and Steckler2009). The ability to resolve either ambiguous emotional cues (Simmons et al., Reference Simmons, Matthews, Feinstein, Hitchcock, Paulus and Stein2008) or conflict from multiple incongruent cues (Etkin, Egner, Peraza, Kandel, & Hirsch, Reference Etkin, Egner, Peraza, Kandel and Hirsch2006) involves dACC/dmPFC interactions with the amygdala, and in anxiety disorders these evaluations may be more likely to be biased to fear responses. In contrast, the ventral medial prefrontal division, which includes rostral anterior cingulate cortex (rACC), subgenual anterior cingulate cortex (sgACC), and ventromedial prefrontal cortex (vmPFC) is involved in emotional regulation (Etkin, Reference Etkin, Stein and Steckler2009). After an amygdala-activating stimulus is determined to be non-threatening, the emotional state can be modified by these ventral areas via amygdala inhibition; activation in these three ventral areas is inversely correlated with amygdala activation during emotion processing, indicating a regulatory role for ventral ACC/mPFC (Phelps, Delgado, Nearing, & LeDoux, Reference Phelps, Delgado, Nearing and LeDoux2004), and this regulatory role is dysfunctional in many anxiety disorders. Amygdala–striatal networks also appear to play a significant role in regulating emotional responses to threatening stimuli. For example, amygdala–striatal function is associated with the avoidance of novelty and uncertainty (Lago, Davis, Grillon, & Ernst, Reference Lago, Davis, Grillon and Ernst2017; Makovac et al., Reference Makovac, Meeten, Watson, Herman, Garfinkel, Critchley and Ottaviani2016).
Abnormalities of the amygdala have been repeatedly implicated in ASD (Baron-Cohen et al., Reference Baron-Cohen, Ring, Bullmore, Wheelwright, Ashwin and Williams2000). Sparse neuronal density in the amygdala has been observed in adolescents with ASD (Schumann & Amaral, Reference Schumann and Amaral2006), and amygdala enlargement in early development has been linked to the severity of social behavior symptoms (Mosconi et al., Reference Mosconi, Cody-Hazlett, Poe, Gerig, Gimpel-Smith and Piven2009a; Nordahl, Reference Nordahl2012). Amygdala–prefrontal/ventral striatal circuits have been implicated in social–communication abnormalities (Chevallier, Kohls, Troiani, Brodkin, & Schultz, Reference Chevallier, Kohls, Troiani, Brodkin and Schultz2012; Odriozola et al., Reference Odriozola, Dajani, Burrows, Gabard-Durnam, Goodman, Baez and Gee2019; Rausch et al., Reference Rausch, Zhang, Haak, Mennes, Hermans, van Oort and Groen2016; Shen et al., Reference Shen, Li, Keown, Lee, Johnson, Angkustsiri and Nordahl2016), and prefrontal cortical/striatal circuit alterations appear to relate to the severity of RRBs (Agam, Joseph, Barton, & Manoach, Reference Agam, Joseph, Barton and Manoach2010; D'Cruz, Mosconi, Ragozzino, Cook, & Sweeney, Reference D'Cruz, Mosconi, Ragozzino, Cook and Sweeney2016; Delmonte, Gallagher, O'Hanlon, McGrath, & Balsters, Reference Delmonte, Gallagher, O'Hanlon, McGrath and Balsters2013; Thakkar et al., Reference Thakkar, Polli, Joseph, Tuch, Hadjikhani, Barton and Manoach2008). Despite these advances in the mechanistic understanding of anxiety and relevant brain circuitry, systematic investigations of functional connectivity of amygdala–ACC/mPFC and amygdala–striatal pathways and their relationship with anxiety in ASD have not been conducted. Comparing amygdala connectivity between individuals with ASD with and without comorbid anxiety is essential for determining both how ASD symptoms relate to trait anxiety, and the nature of emotion-processing dysfunctions that lead to anxiety in individuals with ASD.
In the current study, we used rs-fMRI to examine the functional connectivity of multiple discrete amygdala–ACC/mPFC and amygdala–striatal connections among individuals with ASD and comorbid anxiety (ASD + Anxiety), individuals with ASD without a reported history of an anxiety disorder (ASD-NoAnx), and typically developing (TD) controls. Consistent with studies of individuals with anxiety disorders without ASD (Kim & Whalen, Reference Kim and Whalen2009; Roy et al., Reference Roy, Fudge, Kelly, Perry, Daniele, Carlisi and Ernst2013), we hypothesized ASD + Anxiety individuals would show decreased functional connectivity between the amygdala and ventral prefrontal/striatal regions (rACC, sgACC, vmPFC, and nucleus accumbens) relative to ASD-NoAnx and TD individuals. We also expected ASD + Anxiety individuals to show decreased connectivity between the amygdala and dorsal prefrontal regions (dACC and dmPFC) relative to ASD-NoAnx and TD individuals, consistent with alterations in cognitive appraisal and emotion-monitoring functions (Etkin, Prater, Schatzberg, Menon, & Greicius, Reference Etkin, Prater, Schatzberg, Menon and Greicius2009; Simmons et al., Reference Simmons, Matthews, Feinstein, Hitchcock, Paulus and Stein2008). To determine the extent to which amygdala–ACC/mPFC and amygdala–striatal connectivity was associated with core clinical symptoms of ASD, we also examined the relationships between connectivity of target amygdala pathways and clinically rated social–communication impairment and RRB symptoms.
Methods
Participants
Resting-state fMRI data from a total of 232 participants from the multi-site ABIDE (https://fcon_1000.projects.nitrc.org/indi/abide/) were analyzed (Di Martino et al., Reference Di Martino, Yan, Li, Denio, Castellanos, Alaerts and Milham2014, Reference Di Martino, O'Connor, Chen, Alaerts, Anderson, Assaf and Milham2017). ABIDE is an open, anonymized neuroimaging database in which all the data were obtained following informed consent/assent procedures that were approved by the human subjects boards at each contributing institution. Three groups were identified from the database, matched on age (5–18 years), gender ratio, and IQ (Table 1, Figure 1): ASD + Anxiety individuals, ASD-NoAnx individuals, and TD controls.
Figure 1. Participant characteristics. The typically developing (TD) control group had higher full-scale IQs than the ASD + Anxiety and ASD-NoAnx groups, which was driven by higher verbal IQs, but not performance IQs. The two ASD groups overlapped in severity of social and communication impairments, but the ASD + Anxiety group had more severe restricted, repetitive behaviors (RRBs). White dots and error bars indicate means and 95% confidence intervals.
Table 1. Clinical and demographic information for ASD + Anxiety individuals, ASD-NoAnx individuals, and typically developing (TD) controls.
Note: Values include means, standard deviations (in parentheses), and 95% confidence intervals (CIs). Degrees of freedom for t tests used the Satterthwaite approximation.
ADI: Autism Diagnostic Interview; ASD: autism spectrum disorder; RRBs: restricted, repetitive behaviors
The ASD + Anxiety group (N = 25) included individuals with a current diagnosis of ASD and a comorbid diagnosis of GAD (N = 12), phobia (N = 14), and/or OCD (N = 3) with no other comorbid psychiatric disorders.
The ASD-NoAnx group (N = 68) included individuals with a current diagnosis of ASD but no comorbid psychiatric diagnosis.
The TD group (N = 139) included individuals with no history of or current psychiatric or developmental disorders.
Participants were included from the six sites that reported an individual with ASD + Anxiety (Erasmus University Medical Center Rotterdam (EMC), Institut Pasteur (IP), Kennedy Krieger Institute (KKI), New York University Langone Medical Center (NYU) (two sites), and Oregon Health and Science University (OHSU)). Comorbid disorders were determined by the research team at each site as part of their standard diagnostic assessments, based on the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) (five sites: IP, KKI, NYU1, NYU2, and OHSU) or the Diagnostic Interview Schedule for Children: Young Child Version (DISC-YC) (one site: EMC). Psychotropic medication use was an exclusionary criteria for all groups, with the exception of stimulants that were withheld for 24–48 hr prior to the MRI scan (ASD + Anxiety N = 2; ASD-NoAnx N = 10). ABIDE subject IDs for each group are included in the supplementary material.
IQ was quantified using the Wechsler Abbreviated Scale of Intelligence (WASI) (N = 105), the Wechsler Intelligence Scale for Children (WISC) IV (N = 85), the Differential Ability Scale (DAS) II (N = 22), the Wechsler Adult Intelligence Scale (WAIS) IV (N = 1), WISC-III (N = 1), or WISC-V (N = 1). Full-scale IQ data were not reported for 17 participants, performance IQ was not reported for 44 participants and verbal IQ was not reported for 60 participants. In the two ASD groups, severity of social impairment, communication impairment, and RRBs were quantified using total scores (diagnostic algorithm) from the Autism Diagnostic Interview-Revised (ADI) (Lord, Rutter, & Le Couteur, Reference Lord, Rutter and Le Couteur1994); ADI data were not available for two ASD + Anxiety and 11 ASD-NoAnx participants.
Data analysis
Rs-fMRI preprocessing
Scanner type and sequence parameters varied across the sites and are detailed in the supplementary material. Resting-state scan lengths also varied across the sites, from 5:07 to 7:55 min (85–180 volumes). Estimated smoothness varied across sites (EMC: M = 10.42, SD = 0.85; IP: M = 7.45, SD = 0.27; KKI: M = 7.15, SD = 0.42; NYU1: M = 6.68, SD = 0.36; NYU2: M = 5.78, SD = 0.23; OHSU: M = 9.30, SD = 0.53). To account for this and other potential sources of variability, the testing site was included as a covariate of non-interest in all analyses. Resting-state fMRI data were analyzed using the Configurable Pipeline for the Analysis of Connectomes (C-PAC) (Yan, Craddock, Zuo, Zang, & Milham, Reference Yan, Craddock, Zuo, Zang and Milham2013), which incorporates neuroimaging tools FSL (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, Reference Jenkinson, Beckmann, Behrens, Woolrich and Smith2012), AFNI (Cox, Reference Cox1996), and ANTs (Avants et al., Reference Avants, Tustison, Stauffer, Song, Wu and Gee2014). Anatomical data were conformed to right/posterior/inferior (RPI) orientation, then registered to a 2 mm MNI152 brain-only template by applying a non-linear transform to the skull-on images using ANTs. Images were then skull-stripped using AFNI's 3dSkullStrip and segmented into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) using FSL's FAST tool. The resulting WM mask was multiplied by a WM prior map that was transformed into individual space using the inverse of the linear transforms previously calculated during the ANTs procedure. A CSF mask was multiplied by a ventricle map derived from the Harvard–Oxford atlas distributed with FSL. Skull-stripped images and grey matter tissue maps were transformed into MNI space at 2 mm resolution.
Functional data were sampled to RPI orientation, the first four volumes were censored, and slice timing correction was applied. Motion correction was performed by first coregistering functional images to the mean image using AFNI 3dvolreg, and then calculating a new mean image and coregistering functional images to the new mean. Functional to anatomical registration was performed using FSL's boundary-based registration and a seven degree of freedom linear transform. Nuisance variable regression was applied to the motion-corrected data and included a second-order polynomial, a 24-parameter motion model (the three translation and three rotation parameters of the current and preceding volume, and their squared values), and five principal components from WM and CSF noise regions of interest (ROIs) (CompCor) (Behzadi, Restom, Liau, & Liu, Reference Behzadi, Restom, Liau and Liu2007) to correct for physiological noise. Two preprocessing pipelines were run in parallel—one that included global signal regression and one without (see Murphy & Fox, Reference Murphy and Fox2017). Residuals of the nuisance variable regression were bandpass filtered (0.001 Hz, 0.1 Hz), written into MNI space at 3 mm resolution, and smoothed using a 6 mm full width at half-maximum (FWHM) kernel.
Connectivity matrix
Timecourses were extracted from preprocessed data in select ROIs. Left and right amygdala ROIs (Figure 2a) were defined using the probabilistic, cytoarchitectonic, Juelich histological atlas (Amunts et al., Reference Amunts, Kedo, Kindler, Pieperhoff, Mohlberg, Shah and Zilles2005). Medial prefrontal cortex (Figure 2c) and basal ganglia (Figure 2b) ROIs were defined using the whole-brain functional atlas Craddock 200 (Craddock, James, Holtzheimer, Hu, & Mayberg, Reference Craddock, James, Holtzheimer, Hu and Mayberg2012), which uses spectral clustering to parcellate the brain into regions with homogeneous connectivity patterns. The five medial prefrontal ROIs (all bilateral) were the dmPFC, dACC, rACC, sgACC, and vmPFC. The six basal ganglia ROIs were the left and right heads of the caudate, nucleus accumbens, and putamen. Further details on ROI definitions are included in the supplementary material.
Figure 2. Region of interest (ROI) definitions. Amygdala ROIs ((a), coronal slice at Y = −7) were defined using the Juelich histological atlas. Basal ganglia ((b), coronal slice at Y = 8) and anterior cingulate/medial prefrontal cortex ((c), saggital slice at X = 0) ROIs were defined using the Craddock 200 functional atlas.
Pairwise correlations were computed between left/right amygdala and each of the ACC, PFC and basal ganglia ROIs. Pearson's correlation coefficients were normalized using the Fisher's R to Z transform, and then converted to Z-scores by dividing by the square root of the variance. Degrees of freedom (i.e., the number of time points) were corrected for autocorrelation in the BOLD signal using a whole-brain Bartlett correction factor individually for each subject (Fox, Snyder, Vincent, Corbetta, & Raichle, Reference Fox, Snyder, Vincent, Corbetta and Raichle2005; Van Dijk et al., Reference Van Dijk, Hedden, Venkataraman, Evans, Lazar and Buckner2010); the implementation is described in the supplementary material. This correction was necessary due to the non-independence of time points in the BOLD signal and the varying run lengths across sites (85–180 volumes), but precluded scrubbing volumes based on motion due to the need for a continuous timecourse in calculating the autocorrelation in the BOLD signal. Motion was accounted for in the analyses by adding mean framewise displacement as an individual-level nuisance predictor in all regression models.
Regression models
Z-score connectivity values were analyzed with linear mixed effect regression models using the lme4 package (Bates, Mächler, Bolker, & Walker, Reference Bates, Mächler, Bolker and Walker2015) in R (R Core Team, 2018). Each of the five ACC and mPFC and three basal ganglia ROIs were examined in a separate model; test statistics were not adjusted for multiple comparisons across these eight models, as each ROI was chosen for its theoretical importance in ASD + Anxiety. Categorical fixed effects were contrast coded, and continuous fixed effects were transformed to Z-scores. Site was included as a nuisance predictor using dummy coding, with NYU1 (the most populous site) as the reference level. For clarity, individual site estimates are not included in the figures of model fixed-effect estimates, but can be found in the full model outputs in the supplementary material. Each model contained fixed effects of amygdala hemisphere (left, −.5; right, +.5), age (linear and quadratic orthogonal polynomials), site (dummy coding), motion (mean framewise displacement), and random intercepts of the subject. All models with basal ganglia ROIs included an additional fixed effect of crossing (ipsilateral, −.5; contralateral, +.5), referring to whether the amygdala ROI and basal ganglia ROI were in ipsilateral or contralateral hemispheres. The effect of group was analyzed using Helmert coding. The first contrast compared the TD control group to the ASD-NoAnx group and the ASD + Anxiety group (TD vs. ASD-NoAnx&ASD + Anx: TD, −2/3; ASD-NoAnx, +1/3; ASD + Anxiety, +1/3), while the second contrast compared the two ASD groups with each other (TD, 0; ASD-NoAnx, −.5; ASD + Anxiety, +.5). The models also included interactions between each group contrast and age, hemisphere, and crossing. Models assessing the relationship between connectivity and ASD symptoms included only ASD-NoAnx and ASD + Anxiety participants, and thus included a single-group fixed-effect contrast (ASD-NoAnx, −.5; ASD + Anxiety, +.5). Clinical severity models also included fixed effects of social impairment, communication impairment, and RRBs (ADI total scores, diagnostic algorithm), as well as interactions between each clinical measure and group. Because ADI scores referred to symptoms at age 4–5, ADI × Age interactions were included as additional fixed effects. Parameter-specific p-values were calculated based on the Kenwood–Rogers approximation for degrees of freedom. Standardized beta coefficients were calculated by scaling coefficients by two standard deviations (Gelman, Reference Gelman2008) using the R package sjPlot (Lüdecke, Reference Lüdecke2019). Individual group comparisons were performed by comparing estimated marginal means of each group from the full model using the R package emmeans (Lenth, Signmann, Love, Buerkner, & Herve, Reference Lenth, Signmann, Love, Buerkner and Herve2019). Additional models included IQ (full-scale, performance, verbal) and Group × IQ interactions as additional fixed effects. However, IQ did not improve the model fit and had small effect sizes; it thus was not included in the models described below in order to maximize participant inclusion (17 individuals were missing full-scale IQ data, 44 were missing performance IQ, and 60 were missing verbal IQ).
Data availability
Raw data were obtained from the freely available ABIDE I and II databases. ABIDE subject IDs used in the current study are listed in the supplementary material.
Results
Amygdala–ACC and amygdala–mPFC connectivity
ASD + Anxiety individuals showed decreased connectivity between amygdala and dorsal ACC regions (including dACC and rACC) relative to both comparison groups: ASD-NoAnx versus ASD + Anxiety t (232) = 3.940dACC, 3.010rACC; p = .0001dACC, .003rACC; TD versus ASD + Anxiety t (232) = 3.396dACC, 2.647rACC; p = .001dACC,.009rACC (see Table S4 (in the supplementary material) and Figures 3, 4). In contrast, ASD + Anxiety individuals did not show any differences in amygdala connectivity with ventral ACC/PFC (sgACC and vmPFC) or dorsal PFC (dmPFC) relative to the ASD-NoAnx and TD control groups: ASD-NoAnx versus ASD + Anxiety t (232) = 0.826sgACC, 0.528vmPFC, 1.171dmPFC; p = .410sgACC, .598vmPFC, .243dmPFC; TD versus ASD + Anxiety t (232) = 0.403sgACC, 0.741vmPFC, 0.655dmPFC; p = .687sgACC, .460vmPFC, .513dmPFC. The ASD-NoAnx group did not show any differences in amygdala connectivity with dorsal or ventral ACC/PFC targets relative to the TD controls: t (232) = 0.087dACC, 0.786dmPFC, 0.588rACC, 0.643sgACC, 0.309vmPFC; p = .385dACC, .433dmPFC, .557rACC, .521sgACC, .758vmPFC. A laterality effect was observed where sgACC and vmPFC had higher connectivity to the left amygdala than the right (Cohen's d effect sizes: 0.58sgACC, 0.41vmPFC) but there were no interactions between amygdala hemisphere and group.
Figure 3. Amygdala–PFC (prefrontal cortex) connectivity. Comorbid anxiety was associated with decreased connectivity between the amygdala and dorsal/rostral anterior cingulate cortex (dACC/rACC). Developmental effects on amygdala connectivity were observed throughout the anterior cingulate cortex (ACC), with age-related decreases in amygdala–dACC/rACC connectivity, and a U-shaped pattern of amygdala–sgACC (subgenual anterior cingulate cortex) connectivity, with lower levels in early adolescence compared to childhood or later adolescence. Amygdala connectivity was left-lateralized for sgACC and vmPFC (ventromedial prefrontal cortex), but did not interact with group. Points are standardized fixed-effect estimates with 95% confidence intervals. Predictors are contrast coded: hemisphere (left, −.5; right, +.5); typically developing (TD) versus ASD-NoAnx&ASD + Anxiety (TD, −2/3; ASD-NoAnx, +1/3; ASD + Anxiety +1/3); ASD-NoAnx versus ASD + Anxiety (TD, −1/3; ASD-NoAnx, −1/3, ASD + Anxiety, +2/3). Age and Age2 are orthogonal polynomials centered in the range 5–18 years. Subjects’ mean framewise displacement (FD) is Z-transformed. *p < .05, **p < .01, ***p < .001.
Figure 4. Amygdala–PFC (prefrontal cortex) connectivity. Top row: comorbid ASD + Anxiety (dark magenta) was associated with decreased connectivity between bilateral amygdala and dorsal/rostral emotional attention/regulation areas (dorsal and rostral anterior cingulate cortex (dACC and rACC)) compared to the ASD-NoAnx and typically developing (TD) control groups. Error bars represent 95% confidence intervals. Age-related changes in amygdala connectivity (second row) were observed throughout the anterior cingulate cortex (ACC) (age was modeled with orthogonal linear and quadratic components). Age-associated connectivity differences were characterized by linear decreases in dACC/rACC, and in sgACC (subgenual anterior cingulate cortex) by a U-shaped curve, with connectivity higher in childhood, decreasing in early adolescence and then beginning to increase in adulthood. Greater social impairment scores on the Revised Autism Diagnostic Interview (ADI) (third row) were associated with decreased connectivity between amygdala and dorsomedial prefrontal cortex (dmPFC)/rACC in the ASD + Anxiety group (dark magenta), but not the ASD-NoAnx group (light blue). Greater communication impairment was associated with increased connectivity between amygdala and rACC in the ASD + Anxiety group, and decreased amygdala–dACC connectivity in the ASD-NoAnx group. Lines in rows 2–5 represent predicted model estimates when all other predictors were set to zero (i.e., mean values).
Amygdala–basal ganglia connectivity
ASD + Anxiety individuals showed decreased connectivity between the amygdala and the nucleus accumbens compared with the ASD-NoAnx individuals: t (232) = 2.150NucAcc, p = .033NucAcc, but no differences were observed between groups in amygdala–caudate or amygdala–putamen connectivity (see Table S6 in the supplementary material and Figures 5, 6). Strong laterality effects were observed for amygdala–putamen connectivity, with greater connectivity from the right versus the left amygdala, and for ipsilateral versus contralateral amygdala–putamen connections. No Group × Hemisphere or Group × Crossing interactions were seen for amygdala connectivity with basal ganglia nuclei.
Figure 5. Amygdala–basal ganglia connectivity. Comorbid ASD + Anxiety was associated with decreased connectivity between the amygdala and the nucleus accumbens compared to ASD-NoAnx individuals. A developmental effect of amygdala–nucleus accumbens connectivity was observed, with linear decreases in connectivity with age. Amygdala–putamen connectivity was right-lateralized and greater for ipsilateral connections, but did not interact with group. Points are standardized fixed-effect estimates with 95% confidence intervals. Predictors are contrast coded: hemisphere (left, −.5; right, +.5); crossing (ipsilateral, −.5; contralateral, +.5); TD versus ASD-NoAnx and ASD + Anxiety (TD, −2/3; ASD-NoAnx, +1/3; ASD + Anxiety, +1/3); ASD-NoAnx versus ASD + Anxiety (TD, −1/3; ASD-NoAnx, −1/3; ASD + Anxiety, +2/3). Age and Age2 are orthogonal polynomials centered in the range 5–18 years. Subjects’ mean framewise displacement (FD) is Z-transformed. *p < .05, **p < .01, ***p < .001.
Figure 6. Amygdala–basal ganglia connectivity. Comorbid ASD + Anxiety was associated with decreased connectivity between the amygdala and the nucleus accumbens compared to ASD-NoAnx individuals. Increased restricted, repetitive behavior (RRB) severity was associated with increased connectivity between the amygdala and the caudate/putamen only for ASD-NoAnx individuals. A linear effect of age was observed for the nucleus accumbens, where amygdala connectivity decreased across development; no interactions with ASD or anxiety were observed. Top row: individual connectivity scores and group means with 95% confidence intervals. Second row: curves represent model fits for each group; age was modelled with orthogonal linear and quadratic components. Third–fifth rows: predicted model estimates across the Revised Autism Diagnostic Interview (ADI) social impairment, communication impairment, and RRB total score ranges when each other predictor was set to zero (i.e., mean values).
Clinical and demographic associations
Age was associated with connectivity between the amygdala and all three ACC regions, but not with PFC connectivity. Amygdala–sgACC connectivity across participants followed a U-shaped pattern with higher connectivity in childhood and early adulthood compared to adolescence (Figure 4; effect size: 0.36linear_age, 0.43quad_age). In dACC and rACC, linear decreases in age-related amygdala connectivity were observed (effect size: 0.28linear_age_dACC, 0.34linear_age _rACC). No diagnostic group differences were seen in the relationships between age and amygdala–ACC/PFC connectivity. In the basal ganglia, increased age was linearly associated with lower amygdala–nucleus accumbens connectivity, but no Group × Age interactions were observed for amygdala–nucleus accumbens connectivity, and age was not associated with amygdala–caudate or amygdala–putamen connectivity.
More severe ADI-rated social abnormalities were associated with decreased connectivity between the amygdala and dmPFC/rACC for individuals with ASD + Anxiety (see Table S5 in the supplementary material and Figures 4, 7; effect size = 0.57dmPFC, 0.40rACC) (dmPFC estimate = −0.77, SE = 0.30, t (80) = −2.58, p = .012; rACC estimate = −0.55, SE = 0.28, t (80) = −1.97, p = .052), but these connections were not associated with social interaction scores in the ASD-NoAnx group (dmPFC estimate = 0.08, SE = 0.17, t (80) = 0.50, p = .618; rACC estimate = 0.01, SE = 0.16, t (80) = 0.04, p = .968). Social abnormalities were not associated with any other amygdala–PFC connections. More severe ADI-rated communication impairments were associated with increased amygdala rACC connectivity for individuals with ASD + Anxiety (see Table S5 and Figures 4, 7; effect size = 0.56) (estimate = 0.61, SE = 0.23, t (80) = 2.61, p = .011), but this relationship was not significant for the ASD-NoAnx group (estimate = −0.08, SE = 0.16, t (80) = −0.50, p = .618). Instead, increased communication impairments were associated with decreases in amygdala–dACC connectivity in the ASD-NoAnx group (effect size = 0.28) (estimate = −0.32, SE = 0.16, t (80) = −1.99, p = .050); this association was not observed in ASD + Anxiety individuals (estimate = 0.03, SE = 0.24, t (80) = 0.12, p = .904).
Figure 7. Relationships between amygdala–PFC (prefrontal cortex) connectivity and clinical ratings in individuals with ASD. Social impairment interacted with group, such that decreased amygdala–dmPFC/rACC (dorsomedial prefrontal cortex/rostral anterior cingulate cortex) connectivities were associated with more severe social impairment only in individuals with ASD + Anxiety. Lower communication skills were associated with increased amygdala–rACC connectivity in individuals with ASD + Anxiety, and decreased amygdala–dACC (dorsal anterior cingulate cortex) connectivity in individuals with ASD-NoAnx. Points are standardized fixed-effect estimates with 95% confidence intervals. Predictors are contrast coded: hemisphere (left, −.5; right, +.5); ASD-NoAnx versus ASD + Anxiety (ASD-NoAnx, −.5; ASD + Anxiety, +.5). Age and Age2 are orthogonal polynomials centered in the range 5–18 years. Subjects’ mean framewise displacement (FD) is Z-transformed. Social, Comm., and RRB are Revised Autism Diagnostic Interview (ADI) total scores for social impairment, communication impairment, and restricted, repetitive behaviors (diagnostic algorithm). Values were scaled by converting to Z-scores using sample mean and SD. *p < .05, **p < .01, ***p < .001.
More severe ADI-rated RRB symptoms were associated with increases in connectivity between the amygdala and the caudate/putamen for ASD-NoAnx individuals (see Table S7 in the supplementary material and Figures 6, 8; effect size: 0.27caudate, 0.02putamen) (caudate estimate = 0.31, SE = 0.12, t (80) = 2.65, p = .010; putamen estimate = 0.30, SE = 0.13, t (80) = 2.31, p = .023), but these associations were not observed in the ASD + Anxiety group (caudate estimate = 0.02, SE = 0.22, t (80) = 0.10, p = .921; putamen estimate = 0.27, SE = 0.24, t (80) = 1.14, p = 0.26). Neither social nor communication scores were associated with amygdala–basal ganglia connectivity in either ASD group.
Figure 8. Amygdala–basal ganglia connectivity and clinical ratings in individuals with ASD. Increases in restricted, repetitive behavior (RRB) symptom severity were associated with amygdala–caudate/putamen connectivity. Points are standardized fixed-effect estimates with 95% confidence intervals. Predictors are contrast coded: hemisphere (left, −.5; right, +.5); crossing (ipsilateral, −.5; contralateral +.5); ASD-NoAnx versus ASD + Anxiety (ASD-NoAnx, −.5; ASD + Anxiety, +.5). Age and Age2 are orthogonal polynomials centered in the range 5–18 years. Subjects’ mean framewise displacement (FD) is Z-transformed. Social, Comm., and RRB are Revised Autism Diagnostic Interview (ADI) total scores for social impairment, communication impairment, and restricted, repetitive behaviors (diagnostic algorithm). Values were scaled by converting to Z-scores using sample mean and SD. *p < .05, **p < .01, ***p < .001.
Discussion
The current study provides new evidence for disrupted functional connectivity between the amygdala and the dorsal division of mPFC in ASD that is specific to patients with a comorbid anxiety disorder. In contrast, amygdala–vmPFC connectivity was relatively unaffected in both ASD subgroups. Based on the ACC/PFC regions examined, these results indicate that clinical anxiety in ASD may predominantly involve disruption of amygdala connections to dorsal ACC/PFC that are involved in emotion-monitoring and appraisal functions. Importantly, we did not see any differences in amygdala–ACC/PFC connectivity between ASD-NoAnx individuals and TD controls, which suggests that alterations of resting-state amygdala–ACC/PFC functional connectivity in ASD are unique to those individuals with a co-occurring anxiety disorder. Consistent with prior work, ASD + Anxiety individuals were rated as having more severe RRBs than ASD-NoAnx individuals, suggesting that anxiety may play a role in the development of RRBs (Cashin & Yorke, Reference Cashin and Yorke2018; Gotham et al., Reference Gotham, Bishop, Hus, Huerta, Lund, Buja and Lord2013; Rodgers et al., Reference Rodgers, Glod, Connolly and McConachie2012). More severe social impairment in ASD + Anxiety individuals was associated with decreased amygdala–dmPFC and amygdala–rACC connectivity; communication skills were associated with amygdala–rACC connectivity in individuals with ASD + Anxiety but with amygdala–dACC connectivity in ASD-NoAnx individuals; and RRB symptoms were associated with amygdala–striatal connectivity in ASD-NoAnx individuals, indicating that atypical development of these brain systems may contribute to a broad range of clinical impairments in ASD.
Amygdala–mPFC connectivity in ASD
Dorsal and ventral divisions of the mPFC have been shown to exert distinct regulatory roles on amygdala reactivity to emotional stimuli. Dorsal ACC/PFC, including dACC and dmPFC, monitor and appraise emotional cues and integrate lateral prefrontal cognitive control processes (Etkin, Reference Etkin, Stein and Steckler2009). Ventral regions, including rACC, sgACC, and vmPFC, integrate contextual cues and down- or up-regulate amygdala responses according to the perceived threat level based on the context (Etkin, Reference Etkin, Stein and Steckler2009). Decreased ventral ACC/PFC–amygdala connectivity, especially within vmPFC networks, is a common feature of anxiety disorders, which results in insufficient down-regulation of amygdala reactivity in non-threatening situations (Kim & Whalen, Reference Kim and Whalen2009; Roy et al., Reference Roy, Fudge, Kelly, Perry, Daniele, Carlisi and Ernst2013).
In contrast to prior findings on individuals with anxiety disorders but no ASD (Kim & Whalen, Reference Kim and Whalen2009; Roy et al., Reference Roy, Fudge, Kelly, Perry, Daniele, Carlisi and Ernst2013), our results demonstrate that ventral ACC/PFC–amygdala connections are relatively unaffected in individuals with ASD and comorbid anxiety, although there was some evidence that amygdala–rACC connectivity was disrupted. Although rACC and the more ventral sgACC and vmPFC have overlapping roles, they are recruited for different types of emotion regulation. The rACC is innervated by the emotion-monitoring circuits of dACC and dmPFC, and it plays a role in emotional regulation when resolving ambiguous or conflicting emotional inputs (Etkin et al., Reference Etkin, Egner, Peraza, Kandel and Hirsch2006). More ventral sgACC and vmPFC systems integrate inputs from sensory processing and memory circuits of the temporal and parietal cortex and are preferentially involved in emotion regulation in response to aversive or threatening cues (Indovina, Robbins, Núñez-Elizalde, Dunn, & Bishop, Reference Indovina, Robbins, Núñez-Elizalde, Dunn and Bishop2011; Motzkin, Philippi, Wolf, Baskaya, & Koenigs, Reference Motzkin, Philippi, Wolf, Baskaya and Koenigs2015).
Selective impairment in rACC is consistent with a pattern of disrupted amygdala connectivity driven by the more dorsal dACC and dmPFC. Together, atypical connectivities of the amygdala and dACC, dmPFC and rACC suggest that, in ASD + Anxiety individuals, emotional monitoring, appraisal, and conflict resolution may be especially impacted. Atypical regulation of subcortical circuits by the dorsal ACC/PFC has been shown to disrupt multiple affective and cognitive processes in ASD, including behavioral flexibility and inhibitory control (D'Cruz et al., Reference D'Cruz, Mosconi, Ragozzino, Cook and Sweeney2016; Mosconi et al., Reference Mosconi, Kay, D'Cruz, Seidenfeld, Guter, Stanford and Sweeney2009b; Voorhies et al., Reference Voorhies, Dajani, Vij, Shankar, Turan and Uddin2018). The possibility of overlapping etiologies for these deficits involving dysfunctional dorsal ACC/PFC highlights the need to integrate anxiety and cognitive control functions of the dorsal ACC/PFC in neurodevelopmental models of ASD.
Amygdala–prefrontal and amygdala–striatal connectivity did not differ between ASD-NoAnx individuals and the TD controls, suggesting that amygdala–ACC/PFC disturbances may be specific to patients with comorbid anxiety conditions. Results of whole-brain functional connectivity studies of ASD have been notably inconsistent (Müller et al., Reference Müller, Shih, Keehn, Deyoe, Leyden and Shukla2011; Picci et al., Reference Picci, Gotts and Scherf2016), and outcomes vary based on analytical approaches (Müller et al., Reference Müller, Shih, Keehn, Deyoe, Leyden and Shukla2011; Nair et al., Reference Nair, Keown, Datko, Shih, Keehn and Müller2014) and sample variability (Linke et al., Reference Linke, Olson, Gao, Fishman and Müller2017). Amygdala connectivity has been repeatedly investigated in ASD, with many findings of amygdala hypoconnectivity with frontal, striatal, and temporal targets compared to TD individuals (Iidaka, Kogata, Mano, & Komeda, Reference Iidaka, Kogata, Mano and Komeda2019; Rausch et al., Reference Rausch, Zhang, Haak, Mennes, Hermans, van Oort and Groen2016, Reference Rausch, Zhang, Beckmann, Buitelaar, Groen and Haak2018; Shen et al., Reference Shen, Li, Keown, Lee, Johnson, Angkustsiri and Nordahl2016), but also examples of amygdala hyperconnectivity in ASD (Kleinhans et al., Reference Kleinhans, Reiter, Neuhaus, Pauley, Martin, Dager and Estes2016; Murphy, Foss-Feig, Kenworthy, Gaillard, & Vaidya, Reference Murphy, Foss-Feig, Kenworthy, Gaillard and Vaidya2012). Notably, when we controlled for common sources of variability in our ASD-NoAnx group, including comorbid conditions and medication use, we did not observe differences in amygdala connectivity compared to TD, but we did replicate general findings of amygdala–PFC and amygdala–ACC hypoconnectivity in our ASD + Anxiety individuals.
Amygdala–striatal connectivity
The amygdala sends efferent projections to ventral and dorsal striatum, and we anticipated that these connections would be altered in both ASD groups (Chevallier et al., Reference Chevallier, Kohls, Troiani, Brodkin and Schultz2012; Lago et al., Reference Lago, Davis, Grillon and Ernst2017; Makovac et al., Reference Makovac, Meeten, Watson, Herman, Garfinkel, Critchley and Ottaviani2016). We found evidence for disrupted connectivity between the amygdala and the nucleus accumbens in individuals with ASD + Anxiety, which is consistent with prior reports of amygdala–striatum hypoconnectivity in anxiety disorders (Göttlich, Krämer, Kordon, Hohagen, & Zurowski, Reference Göttlich, Krämer, Kordon, Hohagen and Zurowski2014; Roy et al., Reference Roy, Fudge, Kelly, Perry, Daniele, Carlisi and Ernst2013). Structural and functional abnormalities of the striatum and of fronto-striatal connectivity have been associated with RRBs in ASD (Abbott et al., Reference Abbott, Linke, Nair, Jahedi, Alba, Keown and Müller2018; Delmonte et al., Reference Delmonte, Gallagher, O'Hanlon, McGrath and Balsters2013; Langen et al., Reference Langen, Bos, Noordermeer, Nederveen, van Engeland and Durston2014; Thakkar et al., Reference Thakkar, Polli, Joseph, Tuch, Hadjikhani, Barton and Manoach2008), and we found some evidence for associations between RRB severity and amygdala–caudate/putamen connectivity, but only in ASD-NoAnx individuals. There are multiple overlapping mechanisms that can contribute to RRBs, such as reduced behavioral flexibility (D'Cruz et al., Reference D'Cruz, Mosconi, Ragozzino, Cook and Sweeney2016) or sensorimotor disruptions (Unruh et al., Reference Unruh, Martin, Magnon, Vaillancourt, Sweeney and Mosconi2019), and our findings suggest differences in some of the systems that contribute to RRBs in individuals with ASD with and without comorbid anxiety.
ASD clinical correlations
We found that reduced amygdala–dmPFC and amygdala–rACC connectivity were associated with more severe ADI-rated social abnormalities in individuals with ASD + Anxiety, and more severe communication impairments were associated with increased amygdala–rACC connectivity. This suggests that dysfunctional amygdala–prefrontal connectivity is a potential key neural mechanism of impaired social–communication processing in individuals with ASD who also have comorbid anxiety. In ASD-NoAnx individuals, amygdala–dACC connectivity was inversely associated with communication impairments. Thus, anxiety related to alteration in amygdala–ACC/PFC circuitry may represent an important contributing factor to social–communication challenges in ASD.
Similar symptom profiles may develop from distinct underlying brain processes in individuals with ASD based on their overall clinical presentation, including comorbid disorders. Our results highlight a distinct mechanism of social–communication impairment in a subgroup of ASD with a comorbid anxiety disorder, suggesting that multiple neurodevelopmental disruptions may contribute to social–communication symptoms in ASD. The pattern of aberrant dorsal/rostral mPFC connectivity with the amygdala suggests that issues with threat evaluation (Simmons et al., Reference Simmons, Matthews, Feinstein, Hitchcock, Paulus and Stein2008) or anticipatory worrying (Barker et al., Reference Barker, Munro, Orlov, Morgenroth, Moser, Eysenck and Allen2018; Makovac et al., Reference Makovac, Meeten, Watson, Herman, Garfinkel, Critchley and Ottaviani2016) may contribute to impaired social interactions in individuals with ASD + Anxiety, which necessitates a different approach than social issues that arise from, for example, issues with social motivation or reward (Chevallier et al., Reference Chevallier, Kohls, Troiani, Brodkin and Schultz2012; Delmonte et al., Reference Delmonte, Balsters, McGrath, Fitzgerald, Brennan, Fagan and Gallagher2012), though the exact relationship is likely to vary between different types of anxiety/OCD.
Limitations
In the current study, comorbid anxiety was broadly characterized to include individuals with diagnoses of GAD, phobia, or OCD. Although these disorders share common neural features, including heightened amygdala reactivity (Etkin & Wager, Reference Etkin and Wager2007), they also have distinct neural underpinnings (Blackford & Pine, Reference Blackford and Pine2012; Goodwin, Reference Goodwin2015) that need to be considered in the context of comorbid ASD. As a result, we investigated the consequences of common neural features across anxiety disorders, but were not equipped to identify the effects of individual disorders, such as striatal effects on RRBs in ASD + OCD, or the associations between ASD + social anxiety and ventral ACC/mPFC. A further limitation in our sample was the lack of specificity in quantifying both anxiety and ASD clinical impairment. Dimensional measures of anxiety were not available, and thus anxiety could only be examined categorically (based on the evaluations of the research team at each site using K-SADS or DISC-YC assessments). Several robust ASD-specific dimensional anxiety assessments recently have been developed, including the ASC-ASD (Rodgers et al., Reference Rodgers, Wigham, McConachie, Freeston, Honey and Parr2016), ADIS/ASD (Kerns, Renno, Kendall, Wood, & Storch, Reference Kerns, Renno, Kendall, Wood and Storch2017), and the PRAS-ASD (Scahill et al., Reference Scahill, Lecavalier, Schultz, Evans, Maddox, Pritchett and Edwards2019), which provide new opportunities to clarify relationships between amygdala–PFC connectivity and anxiety across a broader range of comorbid psychopathology. ASD severity was quantified using the diagnostic tool ADI, which was available for most but not all of the participants. In addition, because individuals from multiple testing sites were combined in order to accumulate a sufficient number of ASD + Anxiety individuals, variability across sites was introduced (owing to differences in scanner parameters or resting-state task instructions), although site distributions were generally balanced across groups. Our sample excluded for the use of psychotropic medications, which are known to have wide-ranging impacts on functional connectivity in ASD that would be difficult to interpret in the context of this multi-site study and without detailed information on dosing and duration (Linke et al., Reference Linke, Olson, Gao, Fishman and Müller2017). However, this decision limits the generalizability of our results to more severely impaired individuals with ASD and more severe anxiety cases.
Despite finding that ASD + Anxiety individuals had more severe RRBs than the ASD-NoAnx group, we did not find any robust associations between RRB severity and amygdala connectivity in ASD + Anxiety individuals, but instead only found amygdala–striatal associations with RRB severity in ASD-NoAnx individuals. This lack of an association in ASD + Anxiety individuals was unexpected, given that repetitive behaviors have been associated with anxiety and the amygdala is structurally connected to striatal and PFC regions that have been implicated in RRBs in ASD. However, there are limitations in how RRBs were quantified in the current study, which should be considered in the context of this null anxiety effect. Due to the nature of the sample, RRBs were quantified by ADI diagnostic score totals, which are a retrospective account of symptom severity at age 4–5. Retrospective reports involve inherent biases that would be mitigated by contemporaneous measurement of RRBs. In addition, participants’ ages ranged from 5–18 years, whereas diagnostic scores refer to symptom severity at age 4–5, and thus the discrepancy between scanning age and symptom measurement age varied across individuals and approached 14 years. This discrepancy is particularly important because RRBs tend to shift over development, with more lower-order RRBs (e.g., sensorimotor mannerisms) observed at younger ages and more higher-order RRBs (e.g., an insistence on sameness) at older ages (Esbensen, Seltzer, Lam, & Bodfish, Reference Esbensen, Seltzer, Lam and Bodfish2009), and the higher-order RRBs, especially insistence on sameness, are most frequently found to be associated with anxiety in ASD (Cashin & Yorke, Reference Cashin and Yorke2018; Gotham et al., Reference Gotham, Bishop, Hus, Huerta, Lund, Buja and Lord2013; Rodgers et al., Reference Rodgers, Glod, Connolly and McConachie2012). Our models incorporated moderating effects of age on the associations between ADI scores and amygdala connectivity, but these age effects are likely to vary across individuals, and it will be important to examine how amygdala–PFC and amygdala–striatal circuits relate to current RRB severity across development for distinct subtypes of RRBs.
Conclusions
The present study demonstrates that comorbid anxiety with ASD is associated with decreases in functional connectivity between the amygdala and dorsal/rostral medial ACC/PFC areas involved in emotional monitoring and regulation and decreases in amygdala–nucleus accumbens connectivity. Importantly, our measures of amygdala connectivity were associated with social–communication impairment in the ASD + Anxiety individuals and, to a lesser degree, in ASD-NoAnx individuals. This suggests that different neural mechanisms may contribute to similar manifestations of core ASD symptomology in different clusters of individuals with ASD. These results have broad implications for the role of anxiety and other comorbid psychiatric conditions in unraveling neurobiological heterogeneity in ASD, and suggests that comorbid conditions are an important factor that should be considered in studies of functional connectivity in ASD. Future directions should examine ASD + Anxiety in more detail, as well as the broader impact of other comorbid conditions on ASD heterogeneity. Our observations of anxiety-specific associations between amygdala connectivity and broadly defined ASD symptom severity provide a strong rationale for examining detailed associations between quantifiable levels of trait anxiety and current ASD social impairment, communication impairment, and RRB severity. In addition, expanding the scope beyond the amygdala will be necessary to provide a clearer view of amygdala, ACC, mPFC, and striatal interactions within a broader network of cognitive control, sensory, and default mode areas in ASD + Anxiety. Beyond anxiety, associations between core ASD symptoms and other commonly comorbid conditions (including ADHD, conduct disorders, and depression) may be useful in understanding the distinct neural mechanisms that can contribute to different phenotypic presentations among individuals with ASD. The different underlying mechanisms have treatment implications as well. Cognitive behavioral therapies that emphasize cognitive control strategies to mitigate anxious symptoms may be especially effective in individuals with dysfunctional amygdala–dmPFC connectivity (Etkin et al., Reference Etkin, Prater, Schatzberg, Menon and Greicius2009), and responsivity to pharmacological treatments for anxiety may be related to amygdala–mPFC functional connectivity alterations (Faria et al., Reference Faria, Åhs, Appel, Linnman, Bani, Bettica and Furmark2014; Whalen et al., Reference Whalen, Johnstone, Somerville, Nitschke, Polis, Alexander and Kalin2008). Understanding comorbid conditions in ASD provides essential knowledge towards mechanistic models of ASD behavior that go beyond the symptoms of the world's many autisms.
Supplementary Material
The supplementary material for this article can be found at https://doi.org/10.1017/S0954579420000772
Acknowledgments
The authors would like to thank Madisen Huscher and Qiying Ye for assistance with data processing. This work was supported by NIMH R01 MH112734 and the University of Kansas IDDRC, U54 HD090216. There were no financial conflicts of interest.
