Many parents of children with autism spectrum disorder (ASD) spot the first signs when their child is still a toddler, by noticing that their child is less interested than other toddlers in people and in social play. These early differences in behavior can have long-term implications for brain development. The brains of toddlers with little interest in social stimuli will receive less social input than those of other toddlers. This will make it even harder for the brain to develop the circuits required to support social skills.

But even among children with ASD, there are large differences in children's interest in the social world. One way of measuring these differences is to track eye movements. Lombardo et al. presented toddlers with and without ASD with images of moving colorful geometric shapes next to videos of dancing children. The majority of toddlers, including most of those with ASD, spent more time looking at the children than the shapes. But about 20% of the toddlers with ASD spent most of their time looking at the shapes. These toddlers also had the most severe social symptoms.

To find out why, Lombardo et al. measured the toddlers' brain activity while they slept. During sleep, or when at rest, the brain shows stereotyped patterns of activity. Groups of brain regions that work together – such as those involved in vision – fire in synchrony. Lombardo et al. found that toddlers who preferred looking at shapes over people showed different patterns of brain activity while asleep compared to other children. In the toddlers who preferred shapes, brain networks involved in social skills were less likely to coordinate their activity with networks that support vision and attention.

These findings suggest there may be multiple subtypes of ASD, with different symptoms resulting from different patterns of brain activity. At present, all children who receive a diagnosis of ASD receive much the same behavioral therapy. But in the future, studies of brain networks could allow children to receive more specific diagnoses. This could in turn lead to more effective and personalized treatments.

Social visual engagement difficulties, defined as lack of preference for social stimuli often combined with a strong preference for and attention towards non-social stimuli (Dawson et al., 1998; Dawson et al., 2004; Klin et al., 2015; Chawarska et al., 2013; Falck-Ytter et al., 2013; Klin et al., 2009; Nakano et al., 2010; Shic et al., 2011; von Hofsten et al., 2009; Pierce et al., 2011a; Pierce et al., 2016a; Jones and Klin, 2013; Falck-Ytter et al., 2018), are key early developmental features of autism spectrum disorders (ASD). These difficulties are central in early ASD screening and diagnostic tools (Lord et al., 2000; Pierce et al., 2011b; Robins et al., 2001). A child’s preferences and attention early in life can potentially have large impact on future development and outcome (Klin et al., 2015). Reduced social visual engagement behaviors actively select and/or neglect specific types of information from the environment as input to the developing brain. A continual stream of atypical non-social input due to reduced social visual engagement in ASD may have detrimental impact on experience-expectant and experience-dependent processes (Greenough et al., 1987; Holtmaat and Svoboda, 2009; Huttenlocher, 2002) that help to sculpt functional specialization and development in the social brain (Klin et al., 2015; Klin et al., 2003; Mundy et al., 2009; Johnson et al., 2015; Johnson, 2017). These features are also highly relevant for early intervention. Many early interventions that show success for some individuals (Bacon et al., 2014; Dawson et al., 2010; Kasari et al., 2006; Pickles et al., 2016) hinge critically upon the idea of changing this attribute of early ASD development. The hope is that early intervention will increase engagement between the child and the social world and enable experience-dependent neuroplasticity to divert a child towards more typical developmental trajectories (Dawson, 2008).

Eye tracking studies of children and adults with ASD have been central in quantifying deficits in social visual engagement (Klin et al., 2009; Nakano et al., 2010; Shic et al., 2011; von Hofsten et al., 2009; Pierce et al., 2011a; Pierce et al., 2016a; Jones and Klin, 2013; Falck-Ytter et al., 2018). Nonetheless, considerable heterogeneity exists across ASD individuals and the early-age neural bases explaining such features and their developmental variability are not well understood (Chita-Tegmark, 2016; Guillon et al., 2014). Disentangling early-age heterogeneity is the foundation for making significant progress towards the goals of stratified psychiatry and precision medicine (Collins and Varmus, 2015; Kapur et al., 2012; Lai et al., 2013; Lombardo et al., 2019) for ASD and is therefore one of the biggest challenges in the field. Attempts to identify strong neural underpinnings behind ASD are hindered by mixing potentially different ASD subtypes with different biology (Lombardo et al., 2015; Lombardo et al., 2018a). Furthermore, the clinical relevance of parsing heterogeneity into discrete subtypes is also highly salient - not all individuals take the same developmental path or have similar outcomes (Lord et al., 2015) and individuals may vary considerably in responsiveness to early intervention (Bacon et al., 2014).

We recently discovered (Pierce et al., 2011a) and then replicated (Pierce et al., 2016a) the finding of one such clinically relevant subtype with marked lack of early social visual engagement. This subtype can be identified and the deficit objectively quantified with a novel eye tracking preferential looking paradigm, the GeoPref Test (Figure 1A). The paradigm displays dynamic non-social colorful geometric patterns side-by-side with social images of happy children in motion. A subset of toddlers comprising around 20% of the ASD population, spend less than 30% of task time looking at social displays and instead prefer to attend to geometric patterns 70–100% of the task time. This degree of marked lack of social visual engagement is seldom seen in non-ASD comparison groups, displaying 98% specificity (Pierce et al., 2016a). Thus, this unique subtype of ASD toddlers, referred to as ‘GeoPref ASD’, displays a specific and extreme lack of preference for socially compelling stimuli. GeoPref ASD was originally identified in early screening population-based samples that have high generalizability across the ASD spectrum (Pierce et al., 2011a). In the largest eye tracking study of ASD to date (Pierce et al., 2016a), we replicated in a large independent sample (n = 334) the same ASD subtype using the original cutoffs derived in our first discovery on this topic (Pierce et al., 2011a). GeoPref ASD toddlers are also more severe on a variety of other clinical behavioral measures, indicating that this subtype is highly clinically relevant beyond social visual engagement (Pierce et al., 2016a). The GeoPref Test has high test-retest reliability (Pierce et al., 2016a) and is simple, fast, and easy to implement, making it a robust behavioral assay for identification of a clinically highly relevant early ASD subtype.

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In the current work, we aimed to identify how intrinsic functional connectivity between neural circuits, as measured by resting state fMRI (rsfMRI), is affected in early ASD development and whether heterogeneity in early social visual engagement is a key factor explaining such connectivity differences. Several networks were examined that have high relevance for early social visual engagement. Primary visual cortex, visual association cortices, and networks involved in attentional or salience processing are of high relevance, given their importance in hierarchical processing of features from social visual stimuli (Haxby et al., 2001; Kriegeskorte et al., 2008; Uddin et al., 2013; Mottron et al., 2006; Felleman and Van Essen, 1991; Yang et al., 2015). Subcortical areas such as the amygdala and ventral striatum are also of relevance given theories about social motivation as a key driver of social engagement difficulties in autism (Chevallier et al., 2012; Mosconi et al., 2009; Elison et al., 2013). A large-scale network, the default mode network, is also key since this network is one of the primary networks of the ‘social brain’ involved in high-level social-cognitive and social-communicative processing (e.g., mentalizing, joint attention) (Lombardo et al., 2010a; Redcay et al., 2013; Van Overwalle, 2009; Schurz et al., 2014; Nummenmaa and Calder, 2009; Eggebrecht et al., 2017; Alcalá-López et al., 2018; Redcay and Schilbach, 2019; Schilbach et al., 2008). We analyzed rsfMRI data in one of the largest and youngest samples of ASD to date and compare ASD to several non-ASD comparison groups – typically developing (TD) toddlers, toddlers with language or general developmental delay (LD/DD) and TD toddlers with an older ASD sibling (TD ASDSib). We also examined how connectivity differences may be better modeled by taking into account heterogeneity in early social visual engagement. Based on prior work theorizing altered connectivity between high-level social-cognitive networks in frontal cortex and posterior networks involved in sensation, perception and attention (Courchesne and Pierce, 2005), we hypothesized that there may be atypical and heterogeneous functional connectivity between higher-level social brain networks such as the default mode network (DMN) and posterior lower-level networks that are integral for visual perception and attention. We further hypothesized that GeoPref ASD toddlers may display the most extreme effects on functional connectivity. Finally, to test the utility of our ASD subtype for predicting individual differences in social-communication symptomatology, we predicted that robust relationships between functional connectivity and social-communication behavior would be apparent only within the GeoPref ASD subtype.

Although social visual engagement difficulties are central early developmental features of ASD with high clinical and translational relevance, the neural bases behind these features are not well understood. Here we find robust evidence in early ASD development for atypical intrinsic functional brain organization involving circuitry relevant to social visual engagement. Central to our findings is the importance of early developing social brain circuitry, the default mode network (DMN) (Lombardo et al., 2010a; Kennedy and Adolphs, 2012; Kennedy and Courchesne, 2008a; Kennedy et al., 2006; Padmanabhan et al., 2017; Lombardo et al., 2011; Lombardo et al., 2010b), and its interactions with more basic neural circuitry involved in visual or attention processes. The DMN has been extensively studied in older ASD patients and has been found to be atypical in multiple studies using resting state and task-based social paradigms (Padmanabhan et al., 2017; Lombardo et al., 2010b; Di Martino et al., 2014; Kennedy and Courchesne, 2008b; Hong et al., 2019; Lai et al., 2019; Holiga et al., 2019). However, a novel addition to this literature is our work here showing that in very early development there is functional hypoconnectivity between DMN and primary visual cortex (PVC) as well as visual association circuitry in occipito-temporal cortex (OTC). Here we find that at this point in early development normative groups (e.g., TD) show robust non-zero DMN-OTC connectivity. In contrast, in older typically-developing samples, similar types of robust functional connectivity relationships between these networks are not extensively reported in literature, as DMN is typically highly segregated and embedded at an opposite pole within functional connectivity gradients from sensory cortices (Smith et al., 2015; Margulies et al., 2016). These effects along with DMN-dorsal attention network (DAN) hypoconnectivity show evidence for a case-control difference compared to TD and other non-ASD comparison toddler age groups, and these effects are prominent across both ASD cohorts with and without eye tracking data. This finding suggests that in ASD within the first 4 years of life the developing ‘social brain’ default mode network is on-average functionally disconnected from visual cortices and the dorsal attention network. This finding is important with respect to why social engagement difficulties are a hallmark symptom in early ASD development and shared across most ASD toddlers.

In contrast to generalized case-control differences, we also observed key evidence that early heterogeneity in social visual engagement is important for explaining functional hypoconnectivity between DMN and OTC. The GeoPref ASD subtype is a primary factor behind DMN-OTC functional hypoconnectivity in ASD and this subtype distinction is a better explanatory model than a traditional case-control model or other transdiagnostic models. While GeoPref ASD is behaviorally more severe on a range of different domains, this functional connectivity difference is specific to GeoPref ASD. Other non-ASD toddlers with general developmental or language delay do not show such effects. Furthermore, the magnitude of DMN-OTC functional hypoconnectivity specific to GeoPref ASD was strongly associated with individual-level variability in social-communication difficulties measured on an ASD ‘gold standard’ diagnostic instrument (i.e. ADOS). While DMN-PVC and DMN-DAN connectivity reductions are also prominent in GeoPref ASD toddlers, it could be DMN-OTC connectivity that has the largest implications for the different clinical presentation specific to this subtype. Early functional hypoconnectivity between DMN-OTC circuitry could have important differential impact for this subtype with regards to further developmental functional specialization of the social brain for social cognitive and social-communicative functions. In contrast, the less affected DMN-OTC circuitry in nonGeo ASD individuals could be an indication of the plasticity in this circuitry as a result of relatively more enhanced social experience in this subtype. Given that many early interventions heavily focus on enhancing early social experience (Bacon et al., 2014; Dawson et al., 2010; Kasari et al., 2006; Pickles et al., 2016; Dawson, 2008), DMN-OTC connectivity may be a key target for better understanding why individuals heavily vary in response to early intervention treatment.

It is unknown what neurobiological processes underlie these differences in DMN connectivity with visual and attention circuitry. Recent evidence from two twin studies suggests that variability in early social visual engagement is underpinned by genetic factors potentially linked to heritable common genetic variants (Constantino et al., 2017; Kennedy et al., 2017). How such potential genetic starting points lead to further pathophysiology up to the behavioral phenotype is still a mystery. However, it is clear from recent work that neural pathophysiology in ASD likely begins at the earliest prenatal periods of development (Courchesne et al., 2011; Stoner et al., 2014; Parikshak et al., 2013; Willsey et al., 2013; Marchetto et al., 2017; Parikshak et al., 2016; Courchesne et al., 2019). This prenatal pathophysiology is diverse but includes increased cell proliferation, aberrant cell migration, and downregulated early axonogenesis during early prenatal periods, whereas starting in later prenatal development and continuing throughout the first years of life there is also downregulation of processes typically important for synapse development (Courchesne et al., 2011; Stoner et al., 2014; Parikshak et al., 2013; Willsey et al., 2013; Marchetto et al., 2017; Parikshak et al., 2016; Courchesne et al., 2019). Microglia activation and upregulation of immune/inflammation and protein synthesis (i.e. translation initiation) processes are prominent in ASD throughout life and could have further potential impact on cell signaling relevant to neuronal, glial, and synaptic development (Parikshak et al., 2016; Lombardo et al., 2017; Lombardo et al., 2018b; Pramparo et al., 2015a; Gupta et al., 2014; Voineagu et al., 2011; Chow et al., 2012; Morgan et al., 2010; Takano, 2015). Collectively, postmortem, genomic and genetic, early-age neuroimaging, animal model and patient induced pluripotent stem cell (iPSC) model studies indicate prenatal and postnatal ASD cortex displays an abnormal overabundance of small and possibly poorly differentiated neurons with undergrown axons (Solso et al., 2016), focal laminar and migration defects, neuroinflammation and aberrant synapse formation and function (Courchesne et al., 2019). It has long been theorized that such early abnormalities could cause disconnection of higher-order frontal social and communication networks from lower-level posterior perceptual networks and thereby impair attention to and integration of relevant social, emotional and communicative events during development in ASD (Courchesne and Pierce, 2005). While these early developmental abnormalities are numerous and diverse, a key goal for future work will be to determine which processes, or collection of processes working together, lead to the early emergence of ASD and different social engagement subtypes. One compelling hypothesis is that prenatal pathophysiology could cause a cascade in early development leading towards the ultimate initiation of atypical social engagement behaviors. Aggregation of common genetic risk associated with atypical social visual engagement (Constantino et al., 2017; Kennedy et al., 2017) may be relevant to atypical prenatal development processes (Courchesne et al., 2019). These atypical prenatal processes could trigger early behavioral adaptation responses that manifest as different ways to explore and sample the social environment in early development (Johnson et al., 2015; Johnson, 2017). Different social visual engagement behavior could then lead to different experience-dependent brain development in different ASD toddlers. The end result of this chain could be the emergence of behavioral and neural subtypes but also with some shared aspects of neural abnormalities that are specific to ASD. It will be crucial for future work to examine what set of genetic risk variants and early prenatal pathophysiology may underpin variation in early social visual engagement in ASD and how such mechanisms may lead to different experience-dependent development and neural circuitry. Work utilizing iPSC modeling to recapitulate some of the early prenatal neurobiological processes occurring in such individuals seems highly appropriate for beginning to answer such questions (Marchetto et al., 2017).

The current work may also be important for explaining how and why early developmental functional specialization of social brain circuitry occurs in typical development, and how it may be atypical in some individuals with ASD. Reduced early social engagement behaviors that are adaptations to atypical prenatal starting points could potentially lead to the construction and long-term maintenance of atypical early developmental environmental niches for an individual (Johnson et al., 2015; Johnson, 2017). An environmental niche constructed and maintained from largely sampling the non-social instead of the social world around an infant could have a large impact on how postnatal neural circuits are formed. It is well known that neural circuits are sculpted by experience, particularly in the early years of life when biological processes such as synaptogenesis, axon expansion, and cell-growth are normally at their peak (Greenough et al., 1987; Holtmaat and Svoboda, 2009; Huttenlocher, 2002; Kang et al., 2011). Selective biases to the type of information sampled from the environment in GeoPref ASD may provide the wrong type of input to facilitate social cognitive and social-communicative functional specialization of circuits such as the DMN. Circuits such as the DMN undergo protracted developmental periods of interactive specialization (Johnson, 2011), whereby specialization is achieved through interactions between circuits. Our work here suggests that functional interaction between visual and social brain circuits may be crucial to early social behavior in this ASD subtype, and may have further impact for aiding the developing specialization of function within social brain circuitry.

In order to put this work into context, there are a variety of strengths, caveats, and limitations to underscore. Methodologically, the findings are bolstered by being one of the largest sample sizes to date of rsfMRI data in very young ASD toddlers. The early age range of our sample stands out with respect to most of the existing rsfMRI evidence in the literature, since a large majority of studies examine much older individuals. An interpretational caveat with studies of older individuals is that it is unclear which effects are due to core pathophysiology relevant to ASD and which emerged at much earlier developmental time-points versus effects that could be explained as adaptation or other compensatory mechanisms that occur later in life and are not necessarily linked to core early neurodevelopmental mechanisms of relevance to ASD. While adaptation and compensatory effects are still likely even at very early ages, the advantage of the early age range in this sample (relative to studies on older individuals) is the enhanced ability to interpret the results with regards to possible core mechanisms that are at work at ages when the earliest behavioral symptoms relevant to ASD diagnosis manifest.

The findings are also bolstered by a methodological strength of making comparisons against other non-ASD comparison groups, such as LD/DD and TD ASDSib. Such non-ASD and non-TD comparison groups are relatively rare compared to most neuroimaging studies in the current literature. Comparisons between such groups allow for added inferences about specificity of effects to ASD or its subtypes and also help to show that these effects are not simply due to being clinically more severe across a range of domains. The sample ascertainment strategy based on early population-screening is another considerable strength that should result in higher generalizability compared with other studies that study a smaller subsection of the population, such as baby sibling studies. In fact, data generated using this unique sample ascertainment strategy revealed that functional connectivity within the examined networks including between the DMN and OTC did not significantly differ between unaffected siblings and typically developing toddlers. Interpretation of this finding warrants further investigation but could suggest that the development of early connectivity between social and visual attention brain networks could be key to whether or not an infant eventually manifests symptoms of ASD.

Finally, our study goes beyond most work using case-control designs and identifies heterogeneity in a relatively infrequent subtype that comprises around 20% of the early ASD population. In this study, the sample size of the GeoPref ASD subtype was relatively small (n = 16). While small sample size can be problematic for reasons of suboptimal coverage of the ASD spectrum in case-control comparisons (Lombardo et al., 2019), our design here confronts this issue head first by parsing some aspect of the heterogeneity in ASD, rather than simply being a small sample size case-control comparison. However, because of the imbalance in sample sizes between GeoPref and nonGeo ASD subtypes, future work is necessary where the sample sizes are more balanced. This will require much effort to enrich samples with the GeoPref ASD subtype, given that it is present in around 20% of the early diagnosed ASD population. Second, while small sample size can be problematic for statistical power reasons, we have shown what are the minimal effect sizes at such sample sizes for adequate statistical power (Figure 2—figure supplement 2), and our estimated effect sizes are well above such minimal effects sizes. The brain-behavior correlation estimates are also supplemented by the reporting of bootstrap confidence intervals. Since small sample sizes can result in inflated estimates of correlation, the bootstrap confidence intervals allow for reporting of the distribution of sample correlation estimates that could have been observed.

Finally, it is important to underscore that the GeoPref ASD subtype is based on one relatively quick and simple early eye tracking test. This test picks up about 20% of all early diagnosed ASD individuals as within this subtype. It could be that integration of a larger battery of eye tracking measures and tests may help enhance sensitivity and detect a larger percentage of individuals that may be part of this subtype (Moore et al., 2018). Furthermore, use of eye tracking measures that go beyond static stimuli and use interaction-based paradigms (Redcay and Schilbach, 2019; Schilbach et al., 2013) may further enhance sensitivity. All of these refinements could potentially aid in the identification of early biological bases behind different ASD subtypes.

In conclusion, we identified that functional hypoconnectivity between ‘social brain’ circuitry, the DMN, and low-level visual networks is highly important in the early development of ASD. While DMN-PVC functional connectivity is reduced on-average across ASD toddlers, DMN-OTC functional connectivity is heavily reduced in the GeoPref ASD subtype. Individual-level variation in DMN-OTC functional hypoconnectivity is associated with the degree of social-communication difficulties, but only within the GeoPref ASD subtype. This subtype can be identified with high levels of precision via a simple eye tracking test of social or non-social visual preferences in very early development (Pierce et al., 2011a). Early social-visual functional hypoconnectivity is a key underlying neurobiological feature describing GeoPref ASD and may be critical for future social-communicative development. Thus, we theorize that a neurobiologically driven bias (particularly from prenatal development) leading towards neglect for the social world in early development, if left untreated, would significantly diminish opportunities for social learning and for bootstrapping experience-dependent change within developing neural circuits. This, we suggest, may limit developmental functional specialization within the social brain and lead towards more permanent long-term social, communicative, and cognitive difficulties. Therefore, it is an important goal to hone in on early-age molecular biomarkers that personalize risk, since very early identification may have larger clinical and translational benefits under such contexts. Early detection and interventions that successfully ameliorate early atypical functional connectivity between social brain and visual circuitry might improve social development and outcome for ASD individuals. Despite the narrow view of some institutions (USPSTF) (Siu et al., 2016), early risk detection is an absolute necessity (Pierce et al., 2016b), as is research to devise effective early interventions tailored to specific ASD individuals.

Tidy data and analysis code are available at https://github.com/mvlombardo/geoprefrsfmri (copy archived at https://github.com/elifesciences-publications/geoprefrsfmri).

1. 1. Alcalá-López D
   2. Smallwood J
   3. Jefferies E
   4. Van Overwalle F
   5. Vogeley K
   6. Mars RB
   7. Turetsky BI
   8. Laird AR
   9. Fox PT
   10. Eickhoff SB
   11. Bzdok D

   (2018) Computing the social brain connectome across systems and states

   Cerebral Cortex 28:2207–2232.

   https://doi.org/10.1093/cercor/bhx121

   • PubMed
   • Google Scholar
2. 1. Bacon EC
   2. Dufek S
   3. Schreibman L
   4. Stahmer AC
   5. Pierce K
   6. Courchesne E

   (2014) Measuring outcome in an early intervention program for toddlers with autism spectrum disorder: use of a Curriculum-Based assessment

   Autism Research and Treatment 2014:1–9.

   https://doi.org/10.1155/2014/964704

   • Google Scholar
3. 1. Burnham KP
   2. Anderson DR

   (2004) Multimodel inference: understanding AIC and BIC in model selection

   Sociological Methods and Research 33:261–304.

   https://doi.org/10.1177/0049124104268644

   • Google Scholar
4. 1. Chawarska K
   2. Macari S
   3. Shic F

   (2013) Decreased spontaneous attention to social scenes in 6-month-old infants later diagnosed with autism spectrum disorders

   Biological Psychiatry 74:195–203.

   https://doi.org/10.1016/j.biopsych.2012.11.022

   • PubMed
   • Google Scholar
5. 1. Chevallier C
   2. Kohls G
   3. Troiani V
   4. Brodkin ES
   5. Schultz RT

   (2012) The social motivation theory of autism

   Trends in Cognitive Sciences 16:231–239.

   https://doi.org/10.1016/j.tics.2012.02.007

   • PubMed
   • Google Scholar
6. 1. Chita-Tegmark M

   (2016) Social attention in ASD: a review and meta-analysis of eye-tracking studies

   Research in Developmental Disabilities 48:79–93.

   https://doi.org/10.1016/j.ridd.2015.10.011

   • PubMed
   • Google Scholar
7. 1. Chow ML
   2. Pramparo T
   3. Winn ME
   4. Barnes CC
   5. Li HR
   6. Weiss L
   7. Fan JB
   8. Murray S
   9. April C
   10. Belinson H
   11. Fu XD
   12. Wynshaw-Boris A
   13. Schork NJ
   14. Courchesne E

   (2012) Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages

   PLOS Genetics 8:e1002592.

   https://doi.org/10.1371/journal.pgen.1002592

   • PubMed
   • Google Scholar
8. 1. Collins FS
   2. Varmus H

   (2015) A new initiative on precision medicine

   New England Journal of Medicine 372:793–795.

   https://doi.org/10.1056/NEJMp1500523

   • PubMed
   • Google Scholar
9. 1. Constantino JN
   2. Kennon-McGill S
   3. Weichselbaum C
   4. Marrus N
   5. Haider A
   6. Glowinski AL
   7. Gillespie S
   8. Klaiman C
   9. Klin A
   10. Jones W

   (2017) Infant viewing of social scenes is under genetic control and is atypical in autism

   Nature 547:340–344.

   https://doi.org/10.1038/nature22999

   • PubMed
   • Google Scholar
10. 1. Courchesne E
    2. Mouton PR
    3. Calhoun ME
    4. Semendeferi K
    5. Ahrens-Barbeau C
    6. Hallet MJ
    7. Barnes CC
    8. Pierce K

    (2011) Neuron number and size in prefrontal cortex of children with autism

    Jama 306:2001–2010.

    https://doi.org/10.1001/jama.2011.1638

    • PubMed
    • Google Scholar
11. 1. Courchesne E
    2. Pramparo T
    3. Gazestani VH
    4. Lombardo MV
    5. Pierce K
    6. Lewis NE

    (2019) The ASD living biology: from cell proliferation to clinical phenotype

    Molecular Psychiatry 24:88–107.

    https://doi.org/10.1038/s41380-018-0056-y

    • PubMed
    • Google Scholar
12. 1. Courchesne E
    2. Pierce K

    (2005) Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection

    Current Opinion in Neurobiology 15:225–230.

    https://doi.org/10.1016/j.conb.2005.03.001

    • PubMed
    • Google Scholar
13. 1. Dawson G
    2. Meltzoff AN
    3. Osterling J
    4. Rinaldi J
    5. Brown E

    (1998) Children with autism fail to orient to naturally occurring social stimuli

    Journal of Autism and Developmental Disorders 28:479–485.

    https://doi.org/10.1023/a:1026043926488

    • PubMed
    • Google Scholar
14. 1. Dawson G
    2. Toth K
    3. Abbott R
    4. Osterling J
    5. Munson J
    6. Estes A
    7. Liaw J

    (2004) Early social attention impairments in autism: social orienting, joint attention, and attention to distress

    Developmental Psychology 40:271–283.

    https://doi.org/10.1037/0012-1649.40.2.271

    • PubMed
    • Google Scholar
15. 1. Dawson G

    (2008) Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder

    Development and Psychopathology 20:775–803.

    https://doi.org/10.1017/S0954579408000370

    • PubMed
    • Google Scholar
16. 1. Dawson G
    2. Rogers S
    3. Munson J
    4. Smith M
    5. Winter J
    6. Greenson J
    7. Donaldson A
    8. Varley J

    (2010) Randomized, controlled trial of an intervention for toddlers with autism: the early start denver model

    Pediatrics 125:e17–e23.

    https://doi.org/10.1542/peds.2009-0958

    • PubMed
    • Google Scholar
17. 1. Di Martino A
    2. Yan CG
    3. Li Q
    4. Denio E
    5. Castellanos FX
    6. Alaerts K
    7. Anderson JS
    8. Assaf M
    9. Bookheimer SY
    10. Dapretto M
    11. Deen B
    12. Delmonte S
    13. Dinstein I
    14. Ertl-Wagner B
    15. Fair DA
    16. Gallagher L
    17. Kennedy DP
    18. Keown CL
    19. Keysers C
    20. Lainhart JE
    21. Lord C
    22. Luna B
    23. Menon V
    24. Minshew NJ
    25. Monk CS
    26. Mueller S
    27. Müller RA
    28. Nebel MB
    29. Nigg JT
    30. O'Hearn K
    31. Pelphrey KA
    32. Peltier SJ
    33. Rudie JD
    34. Sunaert S
    35. Thioux M
    36. Tyszka JM
    37. Uddin LQ
    38. Verhoeven JS
    39. Wenderoth N
    40. Wiggins JL
    41. Mostofsky SH
    42. Milham MP

    (2014) The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

    Molecular Psychiatry 19:659–667.

    https://doi.org/10.1038/mp.2013.78

    • PubMed
    • Google Scholar
18. 1. Eggebrecht AT
    2. Elison JT
    3. Feczko E
    4. Todorov A
    5. Wolff JJ
    6. Kandala S
    7. Adams CM
    8. Snyder AZ
    9. Lewis JD
    10. Estes AM
    11. Zwaigenbaum L
    12. Botteron KN
    13. McKinstry RC
    14. Constantino JN
    15. Evans A
    16. Hazlett HC
    17. Dager S
    18. Paterson SJ
    19. Schultz RT
    20. Styner MA
    21. Gerig G
    22. Das S
    23. Kostopoulos P
    24. Schlaggar BL
    25. Petersen SE
    26. Piven J
    27. Pruett JR

    (2017) Joint attention and brain functional connectivity in infants and toddlers

    Cerebral Cortex 15:1709–1720.

    https://doi.org/10.1093/cercor/bhw403

    • Google Scholar
19. 1. Elison JT
    2. Wolff JJ
    3. Heimer DC
    4. Paterson SJ
    5. Gu H
    6. Hazlett HC
    7. Styner M
    8. Gerig G
    9. Piven J
    10. IBIS Network

    (2013) Frontolimbic neural circuitry at 6 months predicts individual differences in joint attention at 9 months

    Developmental Science 16:186–197.

    https://doi.org/10.1111/desc.12015

    • PubMed
    • Google Scholar
20. 1. Falck-Ytter T
    2. Rehnberg E
    3. Bölte S

    (2013) Lack of visual orienting to biological motion and audiovisual synchrony in 3-year-olds with autism

    PLOS ONE 8:e68816.

    https://doi.org/10.1371/journal.pone.0068816

    • PubMed
    • Google Scholar
21. 1. Falck-Ytter T
    2. Nyström P
    3. Gredebäck G
    4. Gliga T
    5. Bölte S
    6. EASE team

    (2018) Reduced orienting to audiovisual synchrony in infancy predicts autism diagnosis at 3 years of age

    Journal of Child Psychology and Psychiatry 59:872–880.

    https://doi.org/10.1111/jcpp.12863

    • PubMed
    • Google Scholar
22. 1. Felleman DJ
    2. Van Essen DC

    (1991) Distributed hierarchical processing in the primate cerebral cortex

    Cerebral Cortex 1:1–47.

    https://doi.org/10.1093/cercor/1.1.1

    • PubMed
    • Google Scholar
23. 1. Greenough WT
    2. Black JE
    3. Wallace CS

    (1987) Experience and brain development

    Child Development 58:539–559.

    https://doi.org/10.2307/1130197

    • PubMed
    • Google Scholar
24. 1. Guillon Q
    2. Hadjikhani N
    3. Baduel S
    4. Rogé B

    (2014) Visual social attention in autism spectrum disorder: insights from eye tracking studies

    Neuroscience & Biobehavioral Reviews 42:279–297.

    https://doi.org/10.1016/j.neubiorev.2014.03.013

    • PubMed
    • Google Scholar
25. 1. Gupta S
    2. Ellis SE
    3. Ashar FN
    4. Moes A
    5. Bader JS
    6. Zhan J
    7. West AB
    8. Arking DE

    (2014) Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism

    Nature Communications 5:5748.

    https://doi.org/10.1038/ncomms6748

    • PubMed
    • Google Scholar
26. 1. Haxby JV
    2. Gobbini MI
    3. Furey ML
    4. Ishai A
    5. Schouten JL
    6. Pietrini P

    (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex

    Science 293:2425–2430.

    https://doi.org/10.1126/science.1063736

    • PubMed
    • Google Scholar
27. 1. Holiga Š
    2. Hipp JF
    3. Chatham CH
    4. Garces P
    5. Spooren W
    6. D'Ardhuy XL
    7. Bertolino A
    8. Bouquet C
    9. Buitelaar JK
    10. Bours C
    11. Rausch A
    12. Oldehinkel M
    13. Bouvard M
    14. Amestoy A
    15. Caralp M
    16. Gueguen S
    17. Ly-Le Moal M
    18. Houenou J
    19. Beckmann CF
    20. Loth E
    21. Murphy D
    22. Charman T
    23. Tillmann J
    24. Laidi C
    25. Delorme R
    26. Beggiato A
    27. Gaman A
    28. Scheid I
    29. Leboyer M
    30. d'Albis MA
    31. Sevigny J
    32. Czech C
    33. Bolognani F
    34. Honey GD
    35. Dukart J

    (2019) Patients with autism spectrum disorders display reproducible functional connectivity alterations

    Science Translational Medicine 11:eaat9223.

    https://doi.org/10.1126/scitranslmed.aat9223

    • PubMed
    • Google Scholar
28. 1. Holtmaat A
    2. Svoboda K

    (2009) Experience-dependent structural synaptic plasticity in the mammalian brain

    Nature Reviews Neuroscience 10:647–658.

    https://doi.org/10.1038/nrn2699

    • PubMed
    • Google Scholar
29. 1. Hong SJ
    2. Vos de Wael R
    3. Bethlehem RAI
    4. Lariviere S
    5. Paquola C
    6. Valk SL
    7. Milham MP
    8. Di Martino A
    9. Margulies DS
    10. Smallwood J
    11. Bernhardt BC

    (2019) Atypical functional connectome hierarchy in autism

    Nature Communications 10:1022.

    https://doi.org/10.1038/s41467-019-08944-1

    • PubMed
    • Google Scholar
30. Book

    1. Huttenlocher PR

    (2002)

    Neural Plasticity: The Effects of Environment on the Development of Cerebral Cortex

    Cambridge: Harvard University Press.

    • Google Scholar
31. 1. Johnson MH

    (2011) Interactive specialization: a domain-general framework for human functional brain development?

    Developmental Cognitive Neuroscience 1:7–21.

    https://doi.org/10.1016/j.dcn.2010.07.003

    • PubMed
    • Google Scholar
32. 1. Johnson MH
    2. Jones EJ
    3. Gliga T

    (2015) Brain adaptation and alternative developmental trajectories

    Development and Psychopathology 27:425–442.

    https://doi.org/10.1017/S0954579415000073

    • PubMed
    • Google Scholar
33. 1. Johnson MH

    (2017) Autism as an adaptive common variant pathway for human brain development

    Developmental Cognitive Neuroscience 25:5–11.

    https://doi.org/10.1016/j.dcn.2017.02.004

    • PubMed
    • Google Scholar
34. 1. Jones W
    2. Klin A

    (2013) Attention to eyes is present but in decline in 2-6-month-old infants later diagnosed with autism

    Nature 504:427–431.

    https://doi.org/10.1038/nature12715

    • PubMed
    • Google Scholar
35. 1. Kang HJ
    2. Kawasawa YI
    3. Cheng F
    4. Zhu Y
    5. Xu X
    6. Li M
    7. Sousa AM
    8. Pletikos M
    9. Meyer KA
    10. Sedmak G
    11. Guennel T
    12. Shin Y
    13. Johnson MB
    14. Krsnik Z
    15. Mayer S
    16. Fertuzinhos S
    17. Umlauf S
    18. Lisgo SN
    19. Vortmeyer A
    20. Weinberger DR
    21. Mane S
    22. Hyde TM
    23. Huttner A
    24. Reimers M
    25. Kleinman JE
    26. Sestan N

    (2011) Spatio-temporal transcriptome of the human brain

    Nature 478:483–489.

    https://doi.org/10.1038/nature10523

    • PubMed
    • Google Scholar
36. 1. Kapur S
    2. Phillips AG
    3. Insel TR

    (2012) Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?

    Molecular Psychiatry 17:1174–1179.

    https://doi.org/10.1038/mp.2012.105

    • PubMed
    • Google Scholar
37. 1. Kasari C
    2. Freeman S
    3. Paparella T

    (2006) Joint attention and symbolic play in young children with autism: a randomized controlled intervention study

    Journal of Child Psychology and Psychiatry 47:611–620.

    https://doi.org/10.1111/j.1469-7610.2005.01567.x

    • PubMed
    • Google Scholar
38. 1. Kennedy DP
    2. Redcay E
    3. Courchesne E

    (2006) Failing to deactivate: resting functional abnormalities in autism

    PNAS 103:8275–8280.

    https://doi.org/10.1073/pnas.0600674103

    • PubMed
    • Google Scholar
39. 1. Kennedy DP
    2. D'Onofrio BM
    3. Quinn PD
    4. Bölte S
    5. Lichtenstein P
    6. Falck-Ytter T

    (2017) Genetic influence on eye movements to complex scenes at short timescales

    Current Biology 27:3554–3560.

    https://doi.org/10.1016/j.cub.2017.10.007

    • PubMed
    • Google Scholar
40. 1. Kennedy DP
    2. Adolphs R

    (2012) The social brain in psychiatric and neurological disorders

    Trends in Cognitive Sciences 16:559–572.

    https://doi.org/10.1016/j.tics.2012.09.006

    • PubMed
    • Google Scholar
41. 1. Kennedy DP
    2. Courchesne E

    (2008a) The intrinsic functional organization of the brain is altered in autism

    NeuroImage 39:1877–1885.

    https://doi.org/10.1016/j.neuroimage.2007.10.052

    • PubMed
    • Google Scholar
42. 1. Kennedy DP
    2. Courchesne E

    (2008b) Functional abnormalities of the default network during self- and other-reflection in autism

    Social Cognitive and Affective Neuroscience 3:177–190.

    https://doi.org/10.1093/scan/nsn011

    • PubMed
    • Google Scholar
43. 1. Kernbach JM
    2. Yeo BTT
    3. Smallwood J
    4. Margulies DS
    5. Thiebaut de Schotten M
    6. Walter H
    7. Sabuncu MR
    8. Holmes AJ
    9. Gramfort A
    10. Varoquaux G
    11. Thirion B
    12. Bzdok D

    (2018) Subspecialization within default mode nodes characterized in 10,000 UK biobank participants

    PNAS 115:12295–12300.

    https://doi.org/10.1073/pnas.1804876115

    • PubMed
    • Google Scholar
44. 1. Klin A
    2. Jones W
    3. Schultz R
    4. Volkmar F

    (2003) The enactive mind, or from actions to cognition: lessons from autism

    Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358:345–360.

    https://doi.org/10.1098/rstb.2002.1202

    • Google Scholar
45. 1. Klin A
    2. Lin DJ
    3. Gorrindo P
    4. Ramsay G
    5. Jones W

    (2009) Two-year-olds with autism orient to non-social contingencies rather than biological motion

    Nature 459:257–261.

    https://doi.org/10.1038/nature07868

    • PubMed
    • Google Scholar
46. 1. Klin A
    2. Shultz S
    3. Jones W

    (2015) Social visual engagement in infants and toddlers with autism: early developmental transitions and a model of pathogenesis

    Neuroscience & Biobehavioral Reviews 50:189–203.

    https://doi.org/10.1016/j.neubiorev.2014.10.006

    • PubMed
    • Google Scholar
47. 1. Kriegeskorte N
    2. Mur M
    3. Ruff DA
    4. Kiani R
    5. Bodurka J
    6. Esteky H
    7. Tanaka K
    8. Bandettini PA

    (2008) Matching categorical object representations in inferior temporal cortex of man and monkey

    Neuron 60:1126–1141.

    https://doi.org/10.1016/j.neuron.2008.10.043

    • PubMed
    • Google Scholar
48. 1. Kundu P
    2. Inati SJ
    3. Evans JW
    4. Luh WM
    5. Bandettini PA

    (2012) Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI

    NeuroImage 60:1759–1770.

    https://doi.org/10.1016/j.neuroimage.2011.12.028

    • PubMed
    • Google Scholar
49. 1. Lai MC
    2. Lombardo MV
    3. Chakrabarti B
    4. Baron-Cohen S

    (2013) Subgrouping the autism "spectrum": reflections on DSM-5

    PLOS Biology 11:e1001544.

    https://doi.org/10.1371/journal.pbio.1001544

    • PubMed
    • Google Scholar
50. 1. Lai MC
    2. Lombardo MV
    3. Chakrabarti B
    4. Ruigrok AN
    5. Bullmore ET
    6. Suckling J
    7. Auyeung B
    8. Happé F
    9. Szatmari P
    10. Baron-Cohen S
    11. MRC AIMS Consortium

    (2019) Neural self-representation in autistic women and association with 'compensatory camouflaging'

    Autism 23:1210–1223.

    https://doi.org/10.1177/1362361318807159

    • PubMed
    • Google Scholar
51. 1. Lombardo MV
    2. Chakrabarti B
    3. Bullmore ET
    4. Wheelwright SJ
    5. Sadek SA
    6. Suckling J
    7. MRC AIMS Consortium
    8. Baron-Cohen S

    (2010a) Shared neural circuits for mentalizing about the self and others

    Journal of Cognitive Neuroscience 22:1623–1635.

    https://doi.org/10.1162/jocn.2009.21287

    • PubMed
    • Google Scholar
52. 1. Lombardo MV
    2. Chakrabarti B
    3. Bullmore ET
    4. Sadek SA
    5. Pasco G
    6. Wheelwright SJ
    7. Suckling J
    8. MRC AIMS Consortium
    9. Baron-Cohen S

    (2010b) Atypical neural self-representation in autism

    Brain 133:611–624.

    https://doi.org/10.1093/brain/awp306

    • PubMed
    • Google Scholar
53. 1. Lombardo MV
    2. Chakrabarti B
    3. Bullmore ET
    4. MRC AIMS Consortium
    5. Baron-Cohen S

    (2011) Specialization of right temporo-parietal junction for mentalizing and its relation to social impairments in autism

    NeuroImage 56:1832–1838.

    https://doi.org/10.1016/j.neuroimage.2011.02.067

    • PubMed
    • Google Scholar
54. 1. Lombardo MV
    2. Pierce K
    3. Eyler LT
    4. Carter Barnes C
    5. Ahrens-Barbeau C
    6. Solso S
    7. Campbell K
    8. Courchesne E

    (2015) Different functional neural substrates for good and poor language outcome in autism

    Neuron 86:567–577.

    https://doi.org/10.1016/j.neuron.2015.03.023

    • PubMed
    • Google Scholar
55. 1. Lombardo MV
    2. Courchesne E
    3. Lewis NE
    4. Pramparo T

    (2017) Hierarchical cortical transcriptome disorganization in autism

    Molecular Autism 8:29.

    https://doi.org/10.1186/s13229-017-0147-7

    • PubMed
    • Google Scholar
56. 1. Lombardo MV
    2. Pramparo T
    3. Gazestani V
    4. Warrier V
    5. Bethlehem RAI
    6. Carter Barnes C
    7. Lopez L
    8. Lewis NE
    9. Eyler L
    10. Pierce K
    11. Courchesne E

    (2018a) Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes

    Nature Neuroscience 21:1680–1688.

    https://doi.org/10.1038/s41593-018-0281-3

    • PubMed
    • Google Scholar
57. 1. Lombardo MV
    2. Moon HM
    3. Su J
    4. Palmer TD
    5. Courchesne E
    6. Pramparo T

    (2018b) Maternal immune activation dysregulation of the fetal brain transcriptome and relevance to the pathophysiology of autism spectrum disorder

    Molecular Psychiatry 23:1001–1013.

    https://doi.org/10.1038/mp.2017.15

    • PubMed
    • Google Scholar
58. 1. Lombardo MV
    2. Lai MC
    3. Baron-Cohen S

    (2019) Big data approaches to decomposing heterogeneity across the autism spectrum

    Molecular Psychiatry 24:1435–1450.

    https://doi.org/10.1038/s41380-018-0321-0

    • Google Scholar
59. Software

    1. Lombardo MV

    (2019) Toddler autism rsfMRI analyses, version a2d8d52

    Github.

    https://github.com/mvlombardo/geoprefrsfmri
60. 1. Lord C
    2. Risi S
    3. Lambrecht L
    4. Cook EH
    5. Leventhal BL
    6. DiLavore PC
    7. Pickles A
    8. Rutter M

    (2000) The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism

    Journal of Autism and Developmental Disorders 30:205–223.

    https://doi.org/10.1023/A:1005592401947

    • PubMed
    • Google Scholar
61. 1. Lord C
    2. Bishop S
    3. Anderson D

    (2015) Developmental trajectories as autism phenotypes

    American Journal of Medical Genetics Part C: Seminars in Medical Genetics 169:198–208.

    https://doi.org/10.1002/ajmg.c.31440

    • Google Scholar
62. 1. Marchetto MC
    2. Belinson H
    3. Tian Y
    4. Freitas BC
    5. Fu C
    6. Vadodaria K
    7. Beltrao-Braga P
    8. Trujillo CA
    9. Mendes APD
    10. Padmanabhan K
    11. Nunez Y
    12. Ou J
    13. Ghosh H
    14. Wright R
    15. Brennand K
    16. Pierce K
    17. Eichenfield L
    18. Pramparo T
    19. Eyler L
    20. Barnes CC
    21. Courchesne E
    22. Geschwind DH
    23. Gage FH
    24. Wynshaw-Boris A
    25. Muotri AR

    (2017) Altered proliferation and networks in neural cells derived from idiopathic autistic individuals

    Molecular Psychiatry 22:820–835.

    https://doi.org/10.1038/mp.2016.95

    • PubMed
    • Google Scholar
63. 1. Margulies DS
    2. Ghosh SS
    3. Goulas A
    4. Falkiewicz M
    5. Huntenburg JM
    6. Langs G
    7. Bezgin G
    8. Eickhoff SB
    9. Castellanos FX
    10. Petrides M
    11. Jefferies E
    12. Smallwood J

    (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization

    PNAS 113:12574–12579.

    https://doi.org/10.1073/pnas.1608282113

    • PubMed
    • Google Scholar
64. 1. Marrelec G
    2. Krainik A
    3. Duffau H
    4. Pélégrini-Issac M
    5. Lehéricy S
    6. Doyon J
    7. Benali H

    (2006) Partial correlation for functional brain interactivity investigation in functional MRI

    NeuroImage 32:228–237.

    https://doi.org/10.1016/j.neuroimage.2005.12.057

    • PubMed
    • Google Scholar
65. 1. Moore A
    2. Wozniak M
    3. Yousef A
    4. Barnes CC
    5. Cha D
    6. Courchesne E
    7. Pierce K

    (2018) The geometric preference subtype in ASD: identifying a consistent, early-emerging phenomenon through eye tracking

    Molecular Autism 9:19.

    https://doi.org/10.1186/s13229-018-0202-z

    • PubMed
    • Google Scholar
66. 1. Morgan JT
    2. Chana G
    3. Pardo CA
    4. Achim C
    5. Semendeferi K
    6. Buckwalter J
    7. Courchesne E
    8. Everall IP

    (2010) Microglial activation and increased microglial density observed in the dorsolateral prefrontal cortex in autism

    Biological Psychiatry 68:368–376.

    https://doi.org/10.1016/j.biopsych.2010.05.024

    • PubMed
    • Google Scholar
67. 1. Mosconi MW
    2. Cody-Hazlett H
    3. Poe MD
    4. Gerig G
    5. Gimpel-Smith R
    6. Piven J

    (2009) Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism

    Archives of General Psychiatry 66:509–516.

    https://doi.org/10.1001/archgenpsychiatry.2009.19

    • PubMed
    • Google Scholar
68. 1. Mottron L
    2. Dawson M
    3. Soulières I
    4. Hubert B
    5. Burack J

    (2006) Enhanced perceptual functioning in autism: an update, and eight principles of autistic perception

    Journal of Autism and Developmental Disorders 36:27–43.

    https://doi.org/10.1007/s10803-005-0040-7

    • PubMed
    • Google Scholar
69. Book

    1. Mullen EM

    (1995)

    Mullen Scales of Early Learning

    Circle Pine: American Guidance Service, Inc.

    • Google Scholar
70. 1. Mundy P
    2. Sullivan L
    3. Mastergeorge AM

    (2009) A parallel and distributed-processing model of joint attention, social cognition and autism

    Autism Research 2:2–21.

    https://doi.org/10.1002/aur.61

    • PubMed
    • Google Scholar
71. 1. Nakano T
    2. Tanaka K
    3. Endo Y
    4. Yamane Y
    5. Yamamoto T
    6. Nakano Y
    7. Ohta H
    8. Kato N
    9. Kitazawa S

    (2010) Atypical gaze patterns in children and adults with autism spectrum disorders dissociated from developmental changes in gaze behaviour

    Proceedings of the Royal Society B: Biological Sciences 277:2935–2943.

    https://doi.org/10.1098/rspb.2010.0587

    • Google Scholar
72. 1. Nummenmaa L
    2. Calder AJ

    (2009) Neural mechanisms of social attention

    Trends in Cognitive Sciences 13:135–143.

    https://doi.org/10.1016/j.tics.2008.12.006

    • PubMed
    • Google Scholar
73. 1. Padmanabhan A
    2. Lynch CJ
    3. Schaer M
    4. Menon V

    (2017) The default mode network in autism

    Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2:476–486.

    https://doi.org/10.1016/j.bpsc.2017.04.004

    • Google Scholar
74. 1. Parikshak NN
    2. Luo R
    3. Zhang A
    4. Won H
    5. Lowe JK
    6. Chandran V
    7. Horvath S
    8. Geschwind DH

    (2013) Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism

    Cell 155:1008–1021.

    https://doi.org/10.1016/j.cell.2013.10.031

    • PubMed
    • Google Scholar
75. 1. Parikshak NN
    2. Swarup V
    3. Belgard TG
    4. Irimia M
    5. Ramaswami G
    6. Gandal MJ
    7. Hartl C
    8. Leppa V
    9. Ubieta LT
    10. Huang J
    11. Lowe JK
    12. Blencowe BJ
    13. Horvath S
    14. Geschwind DH

    (2016) Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism

    Nature 540:423–427.

    https://doi.org/10.1038/nature20612

    • PubMed
    • Google Scholar
76. 1. Patel AX
    2. Kundu P
    3. Rubinov M
    4. Jones PS
    5. Vértes PE
    6. Ersche KD
    7. Suckling J
    8. Bullmore ET

    (2014) A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

    NeuroImage 95:287–304.

    https://doi.org/10.1016/j.neuroimage.2014.03.012

    • PubMed
    • Google Scholar
77. 1. Pickles A
    2. Le Couteur A
    3. Leadbitter K
    4. Salomone E
    5. Cole-Fletcher R
    6. Tobin H
    7. Gammer I
    8. Lowry J
    9. Vamvakas G
    10. Byford S
    11. Aldred C
    12. Slonims V
    13. McConachie H
    14. Howlin P
    15. Parr JR
    16. Charman T
    17. Green J

    (2016) Parent-mediated social communication therapy for young children with autism (PACT): long-term follow-up of a randomised controlled trial

    The Lancet 388:2501–2509.

    https://doi.org/10.1016/S0140-6736(16)31229-6

    • Google Scholar
78. 1. Pierce K
    2. Conant D
    3. Hazin R
    4. Stoner R
    5. Desmond J

    (2011a) Preference for geometric patterns early in life as a risk factor for autism

    Archives of General Psychiatry 68:101–109.

    https://doi.org/10.1001/archgenpsychiatry.2010.113

    • PubMed
    • Google Scholar
79. 1. Pierce K
    2. Carter C
    3. Weinfeld M
    4. Desmond J
    5. Hazin R
    6. Bjork R
    7. Gallagher N

    (2011b) Detecting, studying, and treating autism early: the One-Year Well-Baby Check-Up approach

    The Journal of Pediatrics 159:458–465.

    https://doi.org/10.1016/j.jpeds.2011.02.036

    • Google Scholar
80. 1. Pierce K
    2. Marinero S
    3. Hazin R
    4. McKenna B
    5. Barnes CC
    6. Malige A

    (2016a) Eye tracking reveals abnormal visual preference for geometric images as an early biomarker of an autism spectrum disorder subtype associated with increased symptom severity

    Biological Psychiatry 79:657–666.

    https://doi.org/10.1016/j.biopsych.2015.03.032

    • PubMed
    • Google Scholar
81. 1. Pierce K
    2. Courchesne E
    3. Bacon E

    (2016b) To screen or not to screen universally for autism is not the question: why the Task Force Got It Wrong

    The Journal of Pediatrics 176:182–194.

    https://doi.org/10.1016/j.jpeds.2016.06.004

    • PubMed
    • Google Scholar
82. 1. Power JD
    2. Barnes KA
    3. Snyder AZ
    4. Schlaggar BL
    5. Petersen SE

    (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

    NeuroImage 59:2142–2154.

    https://doi.org/10.1016/j.neuroimage.2011.10.018

    • PubMed
    • Google Scholar
83. 1. Pramparo T
    2. Pierce K
    3. Lombardo MV
    4. Carter Barnes C
    5. Marinero S
    6. Ahrens-Barbeau C
    7. Murray SS
    8. Lopez L
    9. Xu R
    10. Courchesne E

    (2015a) Prediction of autism by translation and immune/inflammation coexpressed genes in toddlers from pediatric community practices

    JAMA Psychiatry 72:386–394.

    https://doi.org/10.1001/jamapsychiatry.2014.3008

    • PubMed
    • Google Scholar
84. 1. Pramparo T
    2. Lombardo MV
    3. Campbell K
    4. Barnes CC
    5. Marinero S
    6. Solso S
    7. Young J
    8. Mayo M
    9. Dale A
    10. Ahrens-Barbeau C
    11. Murray SS
    12. Lopez L
    13. Lewis N
    14. Pierce K
    15. Courchesne E

    (2015b) Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers

    Molecular Systems Biology 11:841.

    https://doi.org/10.15252/msb.20156108

    • PubMed
    • Google Scholar
85. 1. Redcay E
    2. Dodell-Feder D
    3. Mavros PL
    4. Kleiner M
    5. Pearrow MJ
    6. Triantafyllou C
    7. Gabrieli JD
    8. Saxe R

    (2013) Atypical brain activation patterns during a face-to-face joint attention game in adults with autism spectrum disorder

    Human Brain Mapping 34:2511–2523.

    https://doi.org/10.1002/hbm.22086

    • PubMed
    • Google Scholar
86. 1. Redcay E
    2. Schilbach L

    (2019) Using second-person neuroscience to elucidate the mechanisms of social interaction

    Nature Reviews Neuroscience 20:495–505.

    https://doi.org/10.1038/s41583-019-0179-4

    • PubMed
    • Google Scholar
87. 1. Robins DL
    2. Fein D
    3. Barton ML
    4. Green JA

    (2001) The modified checklist for autism in toddlers: an initial study investigating the early detection of autism and pervasive developmental disorders

    Journal of Autism and Developmental Disorders 31:131–144.

    https://doi.org/10.1023/a:1010738829569

    • PubMed
    • Google Scholar
88. 1. Schilbach L
    2. Eickhoff SB
    3. Rotarska-Jagiela A
    4. Fink GR
    5. Vogeley K

    (2008) Minds at rest? social cognition as the default mode of cognizing and its putative relationship to the "default system" of the brain

    Consciousness and Cognition 17:457–467.

    https://doi.org/10.1016/j.concog.2008.03.013

    • PubMed
    • Google Scholar
89. 1. Schilbach L
    2. Timmermans B
    3. Reddy V
    4. Costall A
    5. Bente G
    6. Schlicht T
    7. Vogeley K

    (2013) Toward a second-person neuroscience

    Behavioral and Brain Sciences 36:393–414.

    https://doi.org/10.1017/S0140525X12000660

    • PubMed
    • Google Scholar
90. 1. Schurz M
    2. Radua J
    3. Aichhorn M
    4. Richlan F
    5. Perner J

    (2014) Fractionating theory of mind: a meta-analysis of functional brain imaging studies

    Neuroscience & Biobehavioral Reviews 42:9–34.

    https://doi.org/10.1016/j.neubiorev.2014.01.009

    • PubMed
    • Google Scholar
91. 1. Shic F
    2. Bradshaw J
    3. Klin A
    4. Scassellati B
    5. Chawarska K

    (2011) Limited activity monitoring in toddlers with autism spectrum disorder

    Brain Research 1380:246–254.

    https://doi.org/10.1016/j.brainres.2010.11.074

    • PubMed
    • Google Scholar
92. 1. Siu AL
    2. Bibbins-Domingo K
    3. Grossman DC
    4. Baumann LC
    5. Davidson KW
    6. Ebell M
    7. García FA
    8. Gillman M
    9. Herzstein J
    10. Kemper AR
    11. Krist AH
    12. Kurth AE
    13. Owens DK
    14. Phillips WR
    15. Phipps MG
    16. Pignone MP
    17. US Preventive Services Task Force (USPSTF)

    (2016) Screening for autism spectrum disorder in young children: us preventive services task force recommendation statement

    Jama 315:691–696.

    https://doi.org/10.1001/jama.2016.0018

    • PubMed
    • Google Scholar
93. 1. Smith SM
    2. Miller KL
    3. Salimi-Khorshidi G
    4. Webster M
    5. Beckmann CF
    6. Nichols TE
    7. Ramsey JD
    8. Woolrich MW

    (2011) Network modelling methods for FMRI

    NeuroImage 54:875–891.

    https://doi.org/10.1016/j.neuroimage.2010.08.063

    • PubMed
    • Google Scholar
94. 1. Smith SM
    2. Vidaurre D
    3. Beckmann CF
    4. Glasser MF
    5. Jenkinson M
    6. Miller KL
    7. Nichols TE
    8. Robinson EC
    9. Salimi-Khorshidi G
    10. Woolrich MW
    11. Barch DM
    12. Uğurbil K
    13. Van Essen DC

    (2013) Functional connectomics from resting-state fMRI

    Trends in Cognitive Sciences 17:666–682.

    https://doi.org/10.1016/j.tics.2013.09.016

    • PubMed
    • Google Scholar
95. 1. Smith SM
    2. Nichols TE
    3. Vidaurre D
    4. Winkler AM
    5. Behrens TE
    6. Glasser MF
    7. Ugurbil K
    8. Barch DM
    9. Van Essen DC
    10. Miller KL

    (2015) A positive-negative mode of population covariation links brain connectivity, demographics and behavior

    Nature Neuroscience 18:1565–1567.

    https://doi.org/10.1038/nn.4125

    • PubMed
    • Google Scholar
96. 1. Solso S
    2. Xu R
    3. Proudfoot J
    4. Hagler DJ
    5. Campbell K
    6. Venkatraman V
    7. Carter Barnes C
    8. Ahrens-Barbeau C
    9. Pierce K
    10. Dale A
    11. Eyler L
    12. Courchesne E

    (2016) Diffusion tensor imaging provides evidence of possible axonal overconnectivity in frontal lobes in autism spectrum disorder toddlers

    Biological Psychiatry 79:676–684.

    https://doi.org/10.1016/j.biopsych.2015.06.029

    • PubMed
    • Google Scholar
97. Book

    1. Sparrow S
    2. Balla D
    3. Cicchetti D

    (1984)

    Vineland Scales of Adaptive Behavior: Interview Edition, Survey Form Manual

    Circle Pines: American Guidance Service.

    • Google Scholar
98. 1. Stoner R
    2. Chow ML
    3. Boyle MP
    4. Sunkin SM
    5. Mouton PR
    6. Roy S
    7. Wynshaw-Boris A
    8. Colamarino SA
    9. Lein ES
    10. Courchesne E

    (2014) Patches of disorganization in the neocortex of children with autism

    New England Journal of Medicine 370:1209–1219.

    https://doi.org/10.1056/NEJMoa1307491

    • PubMed
    • Google Scholar
99. 1. Takano T

    (2015) Role of microglia in autism: recent advances

    Developmental Neuroscience 37:195–202.

    https://doi.org/10.1159/000398791

    • PubMed
    • Google Scholar
100. 1. Uddin LQ
     2. Supekar K
     3. Lynch CJ
     4. Khouzam A
     5. Phillips J
     6. Feinstein C
     7. Ryali S
     8. Menon V

     (2013) Salience network-based classification and prediction of symptom severity in children with autism

     JAMA Psychiatry 70:869–879.

     https://doi.org/10.1001/jamapsychiatry.2013.104

     • PubMed
     • Google Scholar
101. 1. Van Overwalle F

     (2009) Social cognition and the brain: a meta-analysis

     Human Brain Mapping 30:829–858.

     https://doi.org/10.1002/hbm.20547

     • PubMed
     • Google Scholar
102. 1. Voineagu I
     2. Wang X
     3. Johnston P
     4. Lowe JK
     5. Tian Y
     6. Horvath S
     7. Mill J
     8. Cantor RM
     9. Blencowe BJ
     10. Geschwind DH

     (2011) Transcriptomic analysis of autistic brain reveals convergent molecular pathology

     Nature 474:380–384.

     https://doi.org/10.1038/nature10110

     • PubMed
     • Google Scholar
103. 1. von Hofsten C
     2. Uhlig H
     3. Adell M
     4. Kochukhova O

     (2009) How children with autism look at events

     Research in Autism Spectrum Disorders 3:556–569.

     https://doi.org/10.1016/j.rasd.2008.12.003

     • Google Scholar
104. 1. Wager TD
     2. Keller MC
     3. Lacey SC
     4. Jonides J

     (2005) Increased sensitivity in neuroimaging analyses using robust regression

     NeuroImage 26:99–113.

     https://doi.org/10.1016/j.neuroimage.2005.01.011

     • PubMed
     • Google Scholar
105. 1. Wetherby AM
     2. Brosnan-Maddox S
     3. Peace V
     4. Newton L

     (2008) Validation of the Infant-Toddler checklist as a broadband screener for autism spectrum disorders from 9 to 24 months of age

     Autism 12:487–511.

     https://doi.org/10.1177/1362361308094501

     • PubMed
     • Google Scholar
106. Book

     1. Wetherby A
     2. Prizant B

     (2002)

     Communication and Symbolic Behavior Scales Developmental Profile, First Normed Edition

     Baltimore: Paul H. Brookes.

     • Google Scholar
107. 1. Willsey AJ
     2. Sanders SJ
     3. Li M
     4. Dong S
     5. Tebbenkamp AT
     6. Muhle RA
     7. Reilly SK
     8. Lin L
     9. Fertuzinhos S
     10. Miller JA
     11. Murtha MT
     12. Bichsel C
     13. Niu W
     14. Cotney J
     15. Ercan-Sencicek AG
     16. Gockley J
     17. Gupta AR
     18. Han W
     19. He X
     20. Hoffman EJ
     21. Klei L
     22. Lei J
     23. Liu W
     24. Liu L
     25. Lu C
     26. Xu X
     27. Zhu Y
     28. Mane SM
     29. Lein ES
     30. Wei L
     31. Noonan JP
     32. Roeder K
     33. Devlin B
     34. Sestan N
     35. State MW

     (2013) Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism

     Cell 155:997–1007.

     https://doi.org/10.1016/j.cell.2013.10.020

     • PubMed
     • Google Scholar
108. 1. Yang DY
     2. Rosenblau G
     3. Keifer C
     4. Pelphrey KA

     (2015) An integrative neural model of social perception, action observation, and theory of mind

     Neuroscience & Biobehavioral Reviews 51:263–275.

     https://doi.org/10.1016/j.neubiorev.2015.01.020

     • PubMed
     • Google Scholar

Author details

1. Michael V Lombardo

   1. Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
   2. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   mvlombardo@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6780-8619

2. Lisa Eyler

   1. Department of Psychiatry, University of California, San Diego, San Diego, United States
   2. VISN 22 Mental Illness Research, Education, and Clinical Center, VA San Diego Healthcare System, San Diego, United States

   Contribution

   Investigation, Methodology, Writing—review and editing, Data collection

   Competing interests

   No competing interests declared

3. Adrienne Moore

   Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States

   Contribution

   Writing—review and editing, Data collection

   Competing interests

   No competing interests declared

4. Michael Datko

   Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States

   Contribution

   Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

5. Cynthia Carter Barnes

   Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States

   Contribution

   Writing—review and editing, Data collection

   Competing interests

   No competing interests declared

6. Debra Cha

   Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States

   Contribution

   Writing—review and editing, Data collection

   Competing interests

   No competing interests declared

7. Eric Courchesne

   Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing, Data collection

   Competing interests

   No competing interests declared

8. Karen Pierce

   Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing, Data collection

   For correspondence

   kpierce@ucsd.edu

   Competing interests

   An invention disclosure form was filed by KP with the University of California, San Diego, on March 5, 2010.

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