Sex differences in functional connectivity between resting state brain networks in Autism Spectrum Disorder

Vol.:(0123456789) 1 3 Journal of Autism and Developmental Disorders https://doi.org/10.1007/s10803-021-05191-6 ORIGINAL PAPER Sex Differences inFunctional Connectivity Between Resting State Brain Networks inAutism Spectrum Disorder VâniaTavares1,2· LuísAfonsoFernandes3· MaríliaAntunes4· HugoFerreira1· DianaPrata1,5,6 Accepted: 6 July 2021 © The Author(s) 2021 Abstract Functional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories. Keywords Functional connectivity· Resting-state networks· Autism spectrum disorder· Independent component analysis· Functional magnetic resonance imaging Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social, behavioral and cognitive impairments (American Psychiatric Association, 2013). The classical categorical system of diagnosing pervasive developmental disorders (i.e. autistic disorder, Asperger’s disorder, pervasive developmental disorder not otherwise specified, childhood disintegrative disorder, and Rett’s disorder) as found in the 4th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) (American Psychiatric Association, 2000), was collapsed into a single dimensional diagnosis of ASD in the 5th edition (DSM-5). The rationale behind this dimension collapse is that the core symptoms exhibited by individuals with ASD are shared across the previous categories, but within a severity degree spectrum. Core symptoms exhibited by an individual with ASD consist of restricted, repetitive, and stereotyped patterns of behavior, as well as impairment in social communication and interaction. A diagnosis of ASD follows a set of criteria based on non-biological, clinically evaluated symptoms (American Psychiatric Association, 2013). There are a number of major cognitive hypotheses for ASD: the ‘Theory of Mind (ToM) dysfunction’ hypothesis (Baron-Cohen Diana Prata and Hugo Ferreira should be considered joint senior authors. * Vânia Tavares vsta[email protected] * Diana Prata [email protected] 1 Faculdade de Ciências, Instituto de Biofísica e Engenharia Biomédica, Universidade de Lisboa, Campo Grande, 1749-016Lisboa, Portugal 2 Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal 3 Serviço de Psiquiatria, Hospital Prof. Doutor Fernando Fonseca, EPE, IC 19, 2720-276Amadora, Portugal 4 Faculdade de Ciências, Centro de Estatística e Aplicações, DEIO, Universidade de Lisboa, Campo Grande 016, 1749-016Lisboa, Portugal 5 Department ofNeuroimaging, Institute ofPsychiatry, Psychology & Neuroscience, King’s College London, London, UK 6 Centro de Investigação e Intervenção Social, Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal Journal of Autism and Developmental Disorders 1 3 etal., 1985), the ‘executive dysfunction’ hypothesis (Ozonoff etal., 1991), the ‘weak central coherence’ hypothesis (Frith, 1989), and the ‘empathizing-systemizing’ hypothesis, also known as the ‘extreme male brain’ theory (BaronCohen, 2009). To master ToM, joint attention and cognitive and emotional empathy are required (Baron-Cohen etal., 1985). Impaired executive function (such as inhibition control, working memory, cognitive flexibility, and planning) is thought to aggravate non-social symptoms (Ozonoff etal., 1991). A more prominent low-level rather than high-level processing system might explain ASD individuals’ improved ability in quantitative tasks, relative to those requiring central coherence, such as visuospatial, auditory-verbal and perceptual tasks (Frith, 1989). Finally, sex differences in ASD prevalence, with the male to female ratio in ASD being 3:1 (Loomes etal., 2017), and in the behavior of typically developing controls (TC) correlate with ASD features. Namely, higher systemizing and lower empathizing ability is common in ASD (vs. TC) and in TC males (vs. TC females). As such, it is hypothesized that individuals with ASD present a shift in the ‘empathizing-systemizing’ continuum towards the systematizing ability (i.e. having a brain more similar to an ‘extreme’ TC male brain) (Baron-Cohen, 2009). To validate and biomark cognitive-behavioral features of ASD, neuroscientific hypotheses supported by neuroimaging have also been put forward. The emerging, and putatively overarching, ‘disrupted connectivity’ (neuroscientific) hypothesis of ASD (Vasa etal., 2016) proposes that clinical symptoms exhibited by ASD individuals have their origin in the way the brain organizes and synchronizes its regions, and is well poised to account for the four above-mentioned cognitive hypotheses and other neuroscientific hypotheses of ASD, such as the ‘salience network dysfunction’ hypothesis (Toyomaki & Murohashi, 2013). This hypothesis postulates that the disrupted connectivity between the salience network (responsible for stimuli salience attribution) and the systems receiving processed stimuli information [the default mode (DM) and executive control networks] leads to social impairments in ASD. Disrupted connectivity can be tested in a resting-state functional magnetic resonance imaging (rs-fMRI) study, such as the present one. A resting-state approach is essential for an unbiased and more comprehensive perspective on brain function which is free from the influence of task-specific confounders, such as task performance differences between ASD and TC (Bressler & Menon, 2010). In this approach, functional brain connectivity (FBC) is measured as the correlation between the spontaneous activity of several brain regions and is compared between ASD and TC. More specifically, one can measure FBC within individual resting-state networks (RSNs) which resemble known spatial topographies of brain activation attributed to task approaches, for example: salience, visual, language, and DM networks (Damoiseaux etal., 2006; Shirer etal., 2012; Smith etal., 2009). Individuals with ASD, across the lifespan, have shown abnormal resting-state brain connectivity within the DM network, predominantly decreased, but also sometimes increased (Hull etal., 2017; Nair etal., 2020). The anterior salience (AS) network within-connectivity has been reported to be increased in children, but decreased in adolescents and adults (Uddin, 2015), which is consistent with the salience network dysfunction (neuroscientific) hypothesis of ASD (Toyomaki & Murohashi, 2013; Uddin & Menon, 2009). Additionally, there is some evidence for reduced long-range and increased short-range connectivity across the lifespan (Rane etal., 2015), consistent with the weak central coherence (cognitive) hypothesis of ASD. Most of these studies performed a within-network FBC analysis using a seed-based (hypothesis-based) approach. The majority of functional connectivity studies in ASD are based on samples composed mainly, or only, of males, most likely due to the unbalanced sex ratio of ASD (Loomes etal., 2017). Nevertheless, the few studies which include both sexes have shown sex-specific differences (i.e. between ASD and TC) in regional functional connectivity, thereby providing growing evidence of an overall hyper-connectivity in females and hypo-connectivity in males, compared to TC (Alaerts etal., 2016; Lawrence etal., 2020; Smith etal., 2019; Ypma etal., 2016), albeit the opposite has also been reported once (Yang & Lee, 2018). For example, when comparing ASD with TC, the connectivity between the cerebellum and several cortical regions (i.e. the bilateral fusiform, the middle occipital, the middle frontal, the precentral gyri, the cingulate cortex, and the precuneus) was found to be increased in females and decreased in males across the lifespan (Smith etal., 2019). However, when compared to TC males, ASD males have shown increased connectivity between brain regions involved in mentalizing processes (i.e. the bilateral temporal-parietal junction), whereas ASD females have shown decreased connectivity (i.e. the medial prefrontal cortex, precuneus, and right temporal-parietal junction) (Yang & Lee, 2018), in a sample of adolescents. The within DM network connectivity has also been shown to be decreased across lifespan in ASD females, when compared to TC females (Ypma etal., 2016). Furthermore, when analyzing diagnosis-specific sex effects on seed-based functional connectivity in children and adolescents, ASD girls have shown increased connectivity between the posterior cingulate cortex (that belongs to the DM network) and the left posterior parietal cortex (that belongs to the central executive network) compared to ASD boys, but with no difference between sexes in TC (Lawrence etal., 2020). Interestingly, TC girls have shown decreased connectivity between the right frontoinsular cortex and the anterior cingulate cortex (that belong to the salience network) compared to TC boys, with no difference between sexes in ASD (Lawrence etal., 2020). Moreover, the within DM network Journal of Autism and Developmental Disorders 1 3 connectivity has been shown to be decreased in TC males when comparing to TC females—with the decrease in connectivity associated with a poorer performance on a mentalizing task (Ypma etal., 2016) in a sample composed of children, adolescents and adults. Furthermore, the main effect of sex on within-network functional connectivity using a sample of children and adolescents with ASD and TC has been explored once, with girls showing increased connectivity within the DM network compared to boys (Olson etal., 2020). More specifically, a few authors have examined FBC between-RSNs using a hypothesis-free approach (Bos etal., 2014; Cerliani etal., 2015; Nomi & Uddin, 2015; Oldehinkel etal., 2019; Olson etal., 2020; von dem Hagen etal., 2013), as we have done in this study. Compared to TC, ASD boys have shown decreased FBC between the executive control network and a network including the cingulate gyrus, in a sample composed of male children and young adolescents (Bos etal., 2014). FBC between salience and DM networks has also been shown to be decreased in male adults with ASD (von dem Hagen etal., 2013). When using a mixed-sex sample, ASD has shown decreased FBC between DM and precuneus (in children) and basal ganglia (BG) networks (in adolescents), whereas no differences were found in betweenRSN FBC in adults (Nomi & Uddin, 2015). Increased FBC between BG and primary sensory [such as primary visual (PV), auditory and sensorimotor] networks and decreased FBC between auditory and sensorimotor networks were also shown in male children, adolescents and adults with ASD (Cerliani etal., 2015). When using a mixed-sex sample of children, adolescents and adults, the FBC between visual network and somatosensory and motor networks was shown to be decreased in ASD, whereas the FBC between cerebellar and sensory (including auditory, language, visual, and somatosensory networks) and motor networks was shown to be increased in ASD (Oldehinkel etal., 2019). Finally, in a study developed in parallel to ours, the main effect of ASD diagnosis and sex and the ASD diagnosis by sex interaction effect on the FBC between-RSNs were explored using a sample of children and adolescents (Olson etal., 2020). All effects were reported to be non-significant (i.e. after correction for multiple comparisons). Remarkably, the above between-RSN FBC findings are inconsistent, possibly due to different subject inclusion choices, such as using: (a) mixed age groups [children, adolescents and adults Cerliani etal., 2015; Oldehinkel etal., 2019), children and adolescents (Bos etal., 2014; Olson etal., 2020), or only children or adolescents or adults (Nomi & Uddin, 2015; von dem Hagen etal., 2013)]; or (b) mixed sex groups (Nomi & Uddin, 2015; Oldehinkel etal., 2019; Olson etal., 2020) or only male subjects (Bos etal., 2014; Cerliani etal., 2015; von dem Hagen etal., 2013). Indeed, previous between-RSN FBC studies have shown differences between ASD and TC populations across lifespan to be agedependent, in particular, showing decreased subcorticocortical connectivity with age (Cerliani etal., 2015) and showing differences in between-RSN FBC (see above) in children and adolescents, but not in adults (Nomi & Uddin, 2015). Additionally, there is also growing evidence that FBC is influenced across the lifespan by sex, both in healthy subjects (Gong etal., 2011; Stumme etal., 2020; Zhang etal., 2016) and in individuals with ASD (Lai etal., 2017; Olson etal., 2020). Therefore, it is important that age and sex are accounted for when performing group comparisons based on FBC measures. Furthermore, only one of the previous between-RSNs studies (Olson etal., 2020), recently published, has explored if ASD-associated effects on betweenRSNs FBC vary depending on the sex of the individuals. In addition to the functional connectivity findings, taskbased functional disturbances in ASD, compared to TC, have also been reported, such as: (a) decreased activation in the medial prefrontal cortex, the superior temporal sulcus, the anterior insula, the anterior cingulate cortex and the amygdala during social processing across the lifespan (Adriana Di Martino etal., 2009; Hernandez etal., 2015); (b) increased activation in BG during cognitive control in adults (Prat etal., 2016), possibly as a compensatory mechanism for cortical malfunction (Subramanian etal., 2017); and (c) a desynchronization of brain regions in language processing in adults (Dichter, 2012). Furthermore, some of the above effects have shown to be modeled by sex. In particular, the decreased activity in the posterior superior temporal sulcus during social processing is present in males, but not in females (when comparing ASD with TC in adults) (Kirkovski etal., 2016). During an empathy task using a sample of adults, ASD males have shown increased activation in the medial frontal gyrus compared to ASD females (an effect not present in the TC group); and ASD females have shown decreased activation in the midbrain and limbic regions compared to TC females (an effect not present in males) (Schneider etal., 2013). In light of the overarching disrupted connectivity hypothesis of autism, and given the above lack of consistency in the literature, we sought to investigate: (1) the main effects of ASD (where we expect to replicate a few previous reports) and of sex on between-RSNs FBC [where we expect to replicate two studies—performed in parallel to our own, one in older TC adults (Stumme etal., 2020) and one in children and adolescents with ASD and TC (Olson etal., 2020)]; and (2) if and how sex influences ASD diagnosis effects (i.e. a diagnosis by sex interaction) on the between-RSNs FBC [attempting to corroborate work which has been performed once, in parallel and independently (Olson etal., 2020), using a sample derived partially from the same original database (ABIDE; see below)]. In the present study, we carefully considered subject eligibility and data acquisition choices to Journal of Autism and Developmental Disorders 1 3 avoid confounding or noise-contributing effects of age, intelligence quotient, handedness, and eye state (open vs. closed) at scan. We compared female and male samples of children, adolescents and adults with ASD with age-matched TC and applied independent component analysis (ICA) on rs-fMRI data. ICA is a fully data-driven method able to identify brain regions that function in a temporally synchronized manner which, in rs-fMRI studies with healthy subjects, correspond to RSNs that resemble task-based functional brain activation (e.g. PV, sensorimotor and executive control networks) (Damoiseaux etal., 2006; Shirer etal., 2012; Smith etal., 2009). Methods Sample Description Data were selected from the Autism Brain Imaging Data Exchange (ABIDE I and II, http:// fcon_ 1000. proje cts. nitrc. org/ indi/ abide/) database (Di Martino etal., 2014) from a pool of 2226 individuals (142 ASD females, 918 ASD males, 280 TC females, and 886 TC males) using the following criteria: (a) having information regarding age at scan, handedness, eye status at scan and full scale intelligence quotient (FIQ); (b) having a FIQ higher than 70; (c) being right-handed; (d) having an anatomical T1-weighted image and an rs-fMRI with a full acquisition length with at least 150 time points and at least 300s and near-full brain coverage; (e) both T1-weighted and rs-fMRI must be free of excessive artifacts (through a visual quality control, see Supplementary methods in the Supplementary material for more details) and rs-fMRI must be successfully registered to the T1-weighted image and to the Montreal Neurological Institute (MNI) template; and (f) having a maximum framewise displacement lower than 3mm (corresponding to the size of one rs-fMRI voxel across the sample). Forty-three females with ASD (the smallest group in the pool) met the above criteria. We then selected 43 ASD males, 43 TC females and 43 TC males from the pool that met the same criteria and had the best match (i.e. lowest difference possible) for age, FIQ, eye state at scan, and mean framewise displacement with the ASD females group. We further excluded 3 ASD females and 1 TC male in order to have no statistically significant difference (p-value < 0.05) in mean framewise displacement between age and eye state at scan between individuals with ASD and TC. Therefore, the final sample included 40 ASD females, 43 ASD males, 43 TC females and 42 TC males. Information regarding the groups, FIQ, autism diagnostic interview-revised (ADI-R), Social Responsiveness Scale, age, mean framewise displacement, and eye state at scan is shown in Table1. For a more detailed sample description and the subject’s IDs included in the sample see the Supplementary methods and the Supplementary Tables S1 and S2 in the Supplementary material. Image Preprocessing Standard preprocessing of functional data was performed using FMRIB Software Library (FSL, www. fmrib. o x. ac. uk/ fsl; Smith etal., 2004) and included a temporal trimming to the first 300s (i.e. 150 time points), removal of the first 3 volumes for signal stabilization, slice timing correction, realignment to the middle volume due to head movement effects, coregistration to the individual anatomical scan and normalization to MNI space (Tzourio-Mazoyer etal., 2002) using a non-linear full-search algorithm with 12 degrees of freedom and data spatial smoothing with a Gaussian 5mm full-weighted at high maximum kernel. Afterwards, data was denoised using ICA-based Automatic Removal Of Motion Artifacts [ICA-AROMA (Pruim etal., 2015)], a data-driven method that identifies and removes head motion related independent components from the rs-fMRI data. Then, the denoised functional scans were high-pass filtered with a cutoff frequency of 0.01Hz. The mean framewise displacement (i.e. head motion) was measured and compared between females and males (p-value = 0.234) and between individuals with ASD and TC [p-value = 0.036; a statistically significant effect driven by the Kennedy Krieger Institute site (KKI), p-value = 0.025]. Therefore, the participants from the KKI site with mean framewise displacement in the lowest/ highest 5-th percentile were discarded (3 ASD females and 1 TC male). Global signal regression was not applied as it has been shown to alter shortand long-range correlations between brain regions, which might potentially introduce spurious group differences in regions where none truly exist (Murphy & Fox, 2017; Saad etal., 2012). Furthermore, it has been recently shown that ICA, as implemented herein, is an efficient method to separate global structured noise from global neural signal (Glasser etal., 2018). Resting‑State Networks Extraction An automatic decomposition of preprocessed functional data was computed using ICA from the FSL Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) version 3.15 tool (Beckmann etal., 2005). The whole dataset underwent a multi-session temporal concatenation analysis (with dimensionality of 20) and 20 z-scored independent maps were obtained. Then, 13 maps were identified as RSNs based on the following criteria: (a) minimal spatial overlap with vascular, ventricular and head-motion susceptible edge regions according to standard guidelines (Kelly etal., 2010); (b) a mean time course’s spectral power with a low-frequency range (0.01 ~ 0.1Hz); and (c) a spatial distribution overlap with Journal of Autism and Developmental Disorders 1 3 RSNs masks downloaded from the Functional Imaging in Neuropsychiatric Disorders (FIND) Lab at Stanford University (Shirer etal., 2012). The discarded maps are described in the Supplementary TableS3 and Supplementary Figure S2, in the Supplementary material. Functional Brain Connectivity andStatistical Analyses A FBC matrix was built for each subject computing the Pearson correlation coefficient for each pair of RSNs mean time courses using FSLNets v0.6 (FMRIB Software Library). These matrices were then normalized using Fisher’s z-transformation to minimize inter-subject correlation variability. Pairwise differences between groups in the correlation coefficient were measured using a general linear model with age, mean framewise displacement, eye state at scan, and site as covariates, and inference was carried out using permutation testing (FSL randomise v2.9 (Winkler etal., 2014), 20 000 permutations) due to the limited sample size. In particular, the following effects on the between-RSN FBC were tested: (a) main effect of diagnosis (ASD vs. TC); (b) main effect Table 1 Participants’ demographics FIQ mean framewise displacement, ADI and Social Responsiveness Scale and Chi-square test for eye state at scan, ADI-R autism diagnostic interview-revised, ASD autism spectrum disorder, C closed eyes, F female, FIQ full scale intelligence quotient, M male, O open eyes, RRB restrictive, repetitive, and stereotyped patterns of behavior *Statistically significant at p-value < 0.05. Group comparisons were made with t-test for independent samples age at scan a Data format: mean (standard deviation); [minimum, maximum]. Information was not available for b8 ASD-F and 4 ASD-M; c9 ASD-F, 12 ASD-M, 15 TC-F, and 17 TC-M participants ASD-F (n = 40) ASD-M (n = 43) TC-F (n = 43) TC-M (n = 42) Group comparison (p-value) Agea (years) 14.7 (6.6) [6.8, 38.8] 12.8 (3.2) [7.3, 20.6] 13.2 (4.2) [5.9, 27.8] 14.3 (5.0) [7.2, 31.8] ASD vs. TD: 0.995 F vs. M: 0.635 ASD.F vs. ASD.M: 0.111 TC.F vs. TC.M: 0.294 ASD.F vs. TC.F: 0.231 ASD.M vs. TC.M: 0.123 FIQa100.8 (14.9) [74, 132] 102.8 (15.2) [72,132] 106.9 (14.1) [80, 132] 103.3 (13.9) [73, 132] ASD vs. TD: 0.138 F vs. M: 0.677 ASD.F vs. ASD.M: 0.546 TC.F vs. TC.M: 0.233 ASD.F vs. TC.F: 0.055 ASD.M vs. TC.M: 0.868 ADI-R sociala,b 17.4 (5.6) [7, 27] 20.1 (5.9) [9, 29] – – ASD.F vs. ASD.M: 0.058 ADI-R verbala,b 13.8 (4.5) [4, 23] 16.5 (4.0) [9, 24] – – ASD.F vs. ASD.M: 0.010* ADI-R RRBa,b 5.1 (2.4) [0, 12] 5.5 (2.6) [1, 12] – – ASD.F vs. ASD.M: 0.568 Social Responsiveness Scalea,c 93.4 (29.9) [17,137] 93.1 (27.9) [42, 155] 19.7 (12.5) [2, 54] 22.4 (19.4) [2, 85] ASD vs. TD: < 0.001* F vs. M: 0.701 ASD.F vs. ASD.M: 0.972 TC.F vs. TC.M: 0.231 ASD.F vs. TC.F: < 0.001* ASD.M vs. TC.M: < 0.001* Eye state 34 O/6 C 37 O/6 C 37 O/6 C 36 O/6 C ASD vs. TD: 0.950 F vs. M: 0.950 ASD.F vs. ASD.M: 0.892 TC.F vs. TC.M: 0.965 ASD.F vs. TC.F: 0.892 ASD.M vs. TC.M: 0.965 Mean framewise displacement (mm) 0.09 (0.05) [0.04, 0.25] 0.09 (0.05) [0.04, 0.23] 0.08 (0.04) [0.03, 0.20] 0.07 (0.05) [0.03, 0.25] ASD vs. TD: 0.109 F vs. M: 0.669 ASD.F vs. ASD.M: 0.766 TC.F vs. TC.M: 0.718 ASD.F vs. TC.F: 0.243 ASD.M vs. TC.M: 0.268 Journal of Autism and Developmental Disorders 1 3 of sex (females vs. males); and (c) interaction effect of diagnosis by sex. For the pairs of RSNs which a main or interaction effect was statistically significant, post hoc pairwise comparisons (e.g. ASD > TC; TC > ASD; males > females; females > males) were tested with a two-sample t-test. Additionally, as a complementary analysis, we also examined whether there were any sex-specific diagnosis effects (i.e. estimated the effect of diagnosis separately in males and in females) or diagnosis-specific sex effects (i.e. estimated the effect of sex in ASD and in TC, separately) on the betweenRSN FBC. Results were considered statistically significant if showing a p-value < 0.05, corrected for multiple comparisons (i.e. multiple pairs of RSNs) with family-wise error rate (FWER). Correlation Analysis Between Functional Brain Connectivity andtheADI‑R Score A Pearson correlation coefficient was computed between the FBC of pairs of RSNs surviving statistical testing (described above) and the ADI-R score for social and communication functions and repetitive, restrictive, and stereotyped patterns of behavior using: (a) only the individuals with ASD (for the main effect of diagnosis); (b) males and females with ASD separately (for the main effect of sex); and (c) only females with ASD (for the female-specific effect of diagnosis). Correlations are considered statistically significant at a Bonferroni corrected (i.e. for 3 multiple comparisons) significance level of p-value < 0.017; or referred to as trends if only surpassing an uncorrected p-value < 0.05. No correlation analysis was conducted between the FBC of pairs of RSNs and the Social Responsiveness Scale. Results Resting‑State Networks Extraction andFBC From the 20 z-scored independent maps, 13 were identified as RSNs comprising AS, auditory (A), BG, cerebellum (C), DM, high visual (HV), language (L), left executive control (LEC), precuneus (P), PV, right executive control (REC), sensorimotor (SM), and visuospatial (VS) networks (Fig.1). Seven maps were discarded as they do not resemble any of the template RSNs [(Shirer etal., 2012); Supplementary TableS3 and Supplementary Figure S2 in the Supplementary material]. Averaged FBC z-scored matrices for ASD-females, ASD-males, TC-females, and TC-males groups are depicted in Supplementary material (Supplementary TableS4 and Supplementary Figure S3). Fig. 1 Spatial configuration of each resting-state network found by independent component analysis Journal of Autism and Developmental Disorders 1 3 Between‑Resting‑State Networks Functional Brain Connectivity Main Effect ofDiagnosis The main effect of diagnosis on the between-RSN FBC was statistically significant in one pair of RSN (Table2): ‘default mode—right executive control’ (FWER-corrected p-value = 0.049), with increased correlation in ASD compared to TC (p-value = 0.001; Fig.2). Furthermore, this difference was also statistically significant when correcting for all the tested RSN-pairs (FWER-corrected p-value = 0.025). See Fand t-statistic, effect size, and uncorrected and FWER-corrected p-values for every RSN pair in Supplementary material, Supplementary TableS5 and S8. Main Effect ofSex The main effect of sex on the between-RSN FBC was statistically significant in one pair of RSNs (Table2): ‘default mode—cerebellum’ (FWER-corrected p-value = 0.046), with increased correlation in males compared to females (p-value = 0.001; Fig.2). Furthermore, this difference was also statistically significant when correcting for all the tested RSNs-pairs (FWER-corrected p-value = 0.024). See Fand t-statistic, effect size, and uncorrected and FWER-corrected p-values for every RSNs pair in Supplementary material, Supplementary TableS6 and S9. Diagnosis bySex Interaction The diagnosis by sex interaction effect on the betweenRSNs FBC was not statistically significant in any pair of RSNs. See F-statistic and uncorrected and FWER-corrected p-values for every RSNs pair in Supplementary material, Supplementary TableS7. Sex‑Specific Effect ofDiagnosis The female-specific effect of diagnosis on the betweenRSN FBC was statistically significant in two pairs of RSNs (Table2): (a) ‘high visual—basal ganglia’, with increased correlation in ASD compared to TC (FWER-corrected p-value = 0.036; Fig.2); and (b) ‘visuospatial—language’, with decreased correlation in ASD compared to TC (FWERcorrected p-value = 0.031; Fig.2). The male-specific effect of diagnosis on the between-RSNs FBC was not statistically significant in any pair of RSNs. See t-statistic, effect size, and uncorrected and FWER-corrected p-values for every RSNs pair in Supplementary material, Supplementary TableS10 and S11. Diagnosis‑Specific Effect ofSex An ASDor TC-specific effect of sex on the between-RSNs FBC was not statistically significant in any pair of RSNs. See t-statistic, effect size, and uncorrected and FWER-corrected p-values for every RSNs pair in Supplementary material, Supplementary TableS12 and S13. Correlation Analysis Between Functional Brain Connectivity andtheADI‑R Score The correlation analysis between the FBC of pairs of RSNs and the ADI-R scores were not statistically significant in any pair of RSNs. See Table3 for full statistics. Discussion The present study compares the between-RSNs FBC (within a set of 13 RSNs and applying ICA to rs-fMRI data) between individuals with ASD and TC, and between sexes, also Table 2 Resting-state network pairs that were found to have a statistically significant effect of diagnosis (i.e. autism spectrum disorder vs. typically developing controls) or sex (females vs. males) at a statistically significant level (FWER-corrected p-value < 0.05 for main effect of diagnosis and sex (i.e. using the whole sample) and femalespecific effect of diagnosis (i.e. using only females) and uncorrected p-value < 0.05 for post hoc comparisons) Main effect of diagnosis Main effect of sex Female-specific effect of diagnosis Default mode—right executive control (F = 12.12, FWER-corrected p-value = 0.049) Default mode—cerebellum (F = 12.22, FWER-corrected p-value = 0.046) Autism spectrum disorder > typically developing controls High visual—basal ganglia (t = 1.45, Cohen’s d = 0.71, FWER-corrected p-value = 0.036) Autism spectrum disorder > typical controls Default mode—right executive control (t = 3.48, Cohen’s d = 0.56 uncorrected p-value = 0.001, FWER-corrected p-value = .025) Males > females Default mode—cerebellum (t = 0.96, Cohen’s d = 0.51, uncorrected p-value = 0.001, FWER-corrected p-value = 0.024) Typically developing controls > autism spectrum disorder Visuospatial—language (t = 3.12, Cohen’s d = 0.68, FWER-corrected p-value = 0.031) Journal of Autism and Developmental Disorders 1 3 Fig. 2 Mean z-scored Pearson correlation coefficients (red dots) per diagnostic [i.e. autism spectrum disorder (ASD) or typically developing controls (TC)] or sex [i.e. females (F) or males (M)] group for each resting-state network pair found to be different at a statistically significant level between the ASD and TC and female and male groups (uncorrected p-value < 0.05), and ASD-females and TCfemales groups (FWER-corrected p-value < 0.05). Blue and black bars represent the standard deviation and error of the mean, respectively. In cases in which the mean was found to be significantly different from zero (tested with a Wilcoxon signed rank test with a p-value < 0.05), an asterisk is shown above the blue bar. BG basal ganglia network; C cerebellum network; DM default mode network; HV high visual network; L language network; REC right executive control network; VS visuospatial network Table 3 Correlation (i.e. Pearson r) between the functional brain connectivity (FBC) of pairs of resting-state networks (RSNs) and the ADI-R score for social and communication (verbal) functions and repetitive, restrictive and stereotyped patterns of behaviors (RRB) Correlations are considered statistically significant at a Bonferroni corrected significance level of p-value < 0.017 and are highlighted with an asterisk. Only the pairs of RSNs surviving statistical comparison of the FBC—aautism spectrum disorder vs. typically developing controls; bfemales vs. males; and cfemales with autism spectrum disorder vs. typically developing female controls—were tested for correlation with ADI-R scores Pairs of resting-state networks ADI-R Social ADI-R Verbal ADI-R RRB Default mode—right executive controla Male + female: r = −0.10, p-value = 0.426 Male + female r = −0.09, p-value = 0.472 Male + female r = −0.00, p-value = 0.972 Default mode—cerebellumbMale: r = 0.23, p-value = 0.168 Female: r = 0.16, p-value = 0.401 Male: r = 0.11, p-value = 0.518 Female: r = 0.14, p-value = 0.444 Male: r = 0.04, p-value = 0.834 Female: r =0 .27, p-value = 0.150 High visual—basal gangliacFemale: r = −0.2, p-value = 0.906 Female: r = −0.11, p-value = 0.570 Female: r = 0.18, p-value = 339 Visuospatial—languagecFemale: r = −0.08, p-value = 0.680 Female: r = −0.8, p-value = 0.656 Female: r = −0.05, p-value = .0.808 Journal of Autism and Developmental Disorders 1 3 exploring a potential modulation of the diagnosis effect by sex. Overall, our results may support the overarching disrupted connectivity hypothesis of ASD, involving the DM network (which showed abnormally increased connectivity with the executive control network in ASD vs. TC, and decreased connectivity with the cerebellum in males vs. females) and involving the HV, BG, visuospatial and language networks (only in females). In relation to specific RSNs, our findings may also support: (a) the executive dysfunction (Ozonoff etal., 1991); (b) the weak central coherence (Borup & Kølgaard, 2014; Frith, 1989); and (c) the empathizing-systemizing (Baron-Cohen, 2009) cognitive hypotheses of ASD, as discussed below. Default Mode Network Hyper‑Connectivity withtheExecutive Control Network inASD Compared toTC We found a main effect of ASD diagnosis on the FBC between the DM and the REC networks, wherein individuals with ASD show higher connectivity than TC with a medium effect size (i.e. 71% of individuals with ASD were above the mean of the TC group’s connectivity [Cohen’s d = 0.56]). Abnormal connectivity involving the same area has been previously found, but in the form of decreased correlation in ASD between the cingulate gyrus network (the posterior part of which belongs to the DM network) and the bilateral executive control networks, in a smaller sample with male children and young adolescents (Bos etal., 2014). Additionally, a trend of decreased correlation in ASD (i.e. an effect that did not survive correction for multiple comparisons) between this pair of networks has been reported in a sample with mixed-sex children and adolescents (Olson etal., 2020). This study was developed in parallel to ours and used a sample partially from ABIDE I and II, i.e. that potentially overlaps with ours, and was of a size similar to ours herein. Moreover, although we do not replicate such findings, decreased between-RSN FBC in ASD has been reported between the DM and: (a) the salience networks [in a smaller sample with male adults (von dem Hagen etal., 2013)]; (b) the precuneus; and (c) the BG [using a smaller sample with mixed-sex children or adolescents, respectively, from the ABIDE I database, i.e. that potentially overlaps with ours; (Nomi & Uddin, 2015)] networks. The other two existing ASD between-RSNs FBC studies employing larger samples ((Oldehinkel etal., 2019)—one which consisted of children, adolescents and adults of both sexes (Cerliani etal., 2015), and another which consisted of male children, adolescents and adults from the ABIDE I database (i.e. potentially overlapping with our sample)—have not implicated the DM nor the executive control networks. The DM network is responsible for social processes and has been extensively studied in ASD (Hull etal., 2017; Nair etal., 2020). It has also been predominantly found to have a decreased within-connectivity in ASD, which is associated with higher severity of ASD symptoms in adolescence and adulthood [for a complete review, see Hull etal., 2017; Nair etal., 2020)]. Furthermore, the DM network was recently reported to be less activated during performance in a mentalizing task, which in turn was associated with social communication deficits in adults with ASD (Hyatt etal., 2020). Furthermore, the executive control network is responsible for cognitive behavior adaptation to internal and external stimuli (Borup & Kølgaard, 2014) and has been found to be less activated in adolescents with ASD during the performance of cognitive control tasks (Solomon etal., 2009). Moreover, it has been hypothesized that the DM and the executive control networks are responsible for processing internal and external stimuli, respectively (Uddin & Menon, 2009). Therefore, disrupted connectivity between these two networks may underlie the impaired cognitive control ability usually seen in ASD (Solomon etal., 2008, 2009) by an inappropriate drive of ASD individuals to internally oriented processes (DM network) or to externally oriented processes (executive control network) (Menon & Uddin, 2010). As such, our results may also support the executive dysfunction cognitive hypothesis in ASD (Ozonoff etal., 1991). Default Mode Network Hyper‑Connectivity withtheCerebellum Network inMales, Compared toFemales We found a main effect of sex on the FBC between the DM and the cerebellum network, with males showing higher connectivity than females with a medium effect size (i.e. 70% of males were above the mean of females’ connectivity [Cohen’s d = 0.51]). Decreased connectivity within the DM network has been previously found in TC males, compared to TC females (Olson etal., 2020; Stumme etal., 2020; Ypma etal., 2016). Additionally, although we do not replicate such findings, increased FBC in TC males compared to TC females has been reported between the DM and the executive control networks in older adults (Stumme etal., 2020). Regarding networks generally, there is growing evidence that females have an intensified network segregation (higher within-network connectivity), whereas males show an intensified network integration (higher between-network connectivity), and this has been suggested to be related to sex differences in behavior (i.e. usually males being better at systematizing tasks—motor and spatial cognitive tasks— and females better at empathizing tasks—emotion identification and nonverbal reasoning) (Satterthwaite etal., 2015; Stumme etal., 2020). Furthermore, the cerebellum has been increasingly implicated in non-motor processes, such as social cognition and language (Sokolov etal., 2017). It has been hypothesized