- Email: [email protected]
Restricted and Repetitive Behavior and Brain Functional Connectivity in Infants at Risk for Developing Autism Spectrum Disorder
Accepted Manuscript Restricted and repetitive behavior and brain functional connectivity in infants at risk for developing autism spectrum disorder Claire J. McKinnon, Adam T. Eggebrecht, Alexandre Todorov, Jason J. Wolff, Jed T. Elison, Chloe M. Adams, Abraham Z. Snyder, Annette M. Estes, Lonnie Zwaigenbaum, Kelly N. Botteron, Robert C. McKinstry, Natasha Marrus, Alan Evans, Heather C. Hazlett, Stephen R. Dager, Sarah J. Paterson, Juhi Pandey, Robert T. Schultz, Martin A. Styner, Guido Gerig, Bradley L. Schlaggar, Steven E. Petersen, Joseph Piven, John R. Pruett, Jr. PII:
S2451-9022(18)30247-7
DOI:
10.1016/j.bpsc.2018.09.008
Reference:
BPSC 339
To appear in:
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Received Date: 2 August 2018 Accepted Date: 1 September 2018
Please cite this article as: McKinnon C.J., Eggebrecht A.T., Todorov A., Wolff J.J., Elison J.T., Adams C.M., Snyder A.Z., Estes A.M., Zwaigenbaum L., Botteron K.N., McKinstry R.C., Marrus N., Evans A., Hazlett H.C., Dager S.R., Paterson S.J., Pandey J., Schultz R.T., Styner M.A., Gerig G., Schlaggar B.L., Petersen S.E., Piven J., Pruett J.R., Jr & for The IBIS Network, Restricted and repetitive behavior and brain functional connectivity in infants at risk for developing autism spectrum disorder, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2018), doi: https://doi.org/10.1016/ j.bpsc.2018.09.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Title: Restricted and repetitive behavior and brain functional connectivity in infants at risk for developing autism spectrum disorder
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Authors: Claire J. McKinnon1,2,†*, Adam T. Eggebrecht3,†, Alexandre Todorov1, Jason J. Wolff4, Jed T. Elison5, Chloe M. Adams1, Abraham Z. Snyder3, Annette M. Estes6, Lonnie Zwaigenbaum7, Kelly N. Botteron1,3, Robert C. McKinstry3, Natasha Marrus1, Alan Evans8, Heather C. Hazlett9, Stephen R. Dager10, Sarah J. Paterson11, Juhi Pandey12, Robert T. Schultz12, Martin A. Styner9, Guido Gerig13, Bradley L. Schlaggar1,3,14, Steven E. Petersen3,14,15,16, and Joseph Piven9,‡ & John R. Pruett, Jr.1,‡ , for The IBIS Network∆ Affiliations: 1
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Department of Psychiatry; Washington University School of Medicine; 660 S. Euclid Ave, St Louis, MO, 63110; USA.
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Biological Sciences Division; University of Chicago; 5841 S. Maryland Ave., Chicago, IL, 60637; USA.
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Mallinckrodt Institute of Radiology; Washington University School of Medicine; 660 S. Euclid Ave, St Louis, MO, 63110; USA.
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Department of Educational Psychology; University of Minnesota; 56 East River Road, Minneapolis, MN, 55455; USA. 5
Institute of Child Development; University of Minnesota; 51 East River Parkway, Minneapolis, MN, 55455; USA.
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Department of Speech and Hearing Sciences; University of Washington, Seattle; UW Autism Center Box 357920, Seattle, WA, 98195; USA.
Department of Pediatrics; University of Alberta; c/o Autism Research Centre, Glenrose Rehabilitation Hospital E209, 10230, 111th Avenue, Edmonton, AB T5G0B7; Canada.
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McConnell Brain Imaging Center; Montreal Neurological Institute; McGill University; 3801 University St., Montreal, QC, H3A 2B4; Canada.
The Carolina Institute for Developmental Disabilities; University of North Carolina at Chapel Hill; 101 Renee Lynn Court, Carborro, NC, 277599-3367; USA. 10
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Department of Radiology and Bioengineering; University of Washington, Seattle; 1701 NE Columbia Rd, Seattle, WA, 98195-7920; USA. Department of Psychology; Temple University; 1701 N. 13th St., Philadelphia, PA, 19122; USA.
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Center for Autism Research, Children’s Hospital of Philadelphia; Department of Psychiatry, University of Pennsylvania; Civic Center Blvd, Philadelphia, PA, 19104; USA.
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Tandon School of Engineering; New York University; 2 Metro Tech Center, Brooklyn, NY, 11201; USA. 14
Department of Neurology; Washington University School of Medicine; 660 S. Euclid Ave, St Louis, MO, 63110; USA.
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Department of Psychological and Brain Sciences; Washington University in St. Louis; 1 Brookings Dr., St Louis, MO, 63130; USA. 16
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Shared first authors
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Shared senior authors
*Correspondence to: Claire McKinnon Biological Sciences Division University of Chicago 5841 S. Maryland Ave. Chicago, IL 60637, USA Phone: 773-795-3849 Email: [email protected]
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See Acknowledgements for information about The Infant Brain Imaging Study (IBIS) Network
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Department of Biomedical Engineering; Washington University in St. Louis; 1 Brookings Dr., St Louis, MO, 63130; USA.
Short title: RRBs and brain fc in infants at risk for developing ASD
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Keywords: Autism spectrum disorder; brain development; restricted and repetitive behavior; functional connectivity; functional magnetic resonance imaging; infant Number of words in abstract: 250
Number of words in main text: 3971
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Number of figures: 6 Number of tables: 1
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Number of supplemental information: 1
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Abstract
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Background—Restricted and repetitive behaviors (RRBs), detectable by 12 months (mo) in
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many infants later diagnosed with autism spectrum disorder (ASD), may represent some of the
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earliest behavioral markers of ASD. However, brain function underlying the emergence of these
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key behaviors remains unknown.
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Methods—Behavioral and resting-state functional connectivity (fc) magnetic resonance imaging
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data were collected from 167 children at high and low familial risk for ASD at 12 and 24
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mo (n=38 at both time points). Fifteen infants met criteria for ASD at 24 mo. We divided RRBs
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into four subcategories (restricted, stereotyped, ritualistic/sameness, self-injurious) and used a
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data-driven approach to identify functional brain networks associated with the development of
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each RRB subcategory.
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Results—Higher scores for ritualistic/sameness behavior were associated with less positive fc
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between visual and control networks at 12 and 24 mo. Ritualistic/sameness and stereotyped
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behaviors were associated with less positive fc between visual and default mode networks at 12
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mo. At 24 mo, stereotyped and restricted behaviors were associated with more positive fc
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between default mode and control networks. Additionally, at 24 mo, stereotyped behavior was
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associated with more positive fc between dorsal attention and subcortical networks, while
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restricted behavior was associated with more positive fc between default mode and dorsal
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attention networks. No significant network-level associations were observed for self-injurious
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behavior.
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Conclusion—These observations mark the earliest known description of functional brain
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systems underlying RRBs, reinforce the construct validity of RRB subcategories in infants, and
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implicate specific neural substrates for future interventions targeting RRBs.
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Introduction
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In the first two years of typical childhood development, restricted and repetitive behaviors
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(RRBs) contribute to a cascade of progressive events that engender flexible and complex patterns
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of goal-directed behavior. However, recent evidence suggests that elevated levels of RRBs
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comprise some of the earliest emerging behavioral manifestations of autism spectrum disorder
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(ASD) and are observed as early as 12 months of age in infants later diagnosed with ASD (1-4).
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Along with deficits in social communication and interaction, RRBs comprise the defining core
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features of ASD (5), a common neurodevelopmental disorder that is among the most highly
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heritable of psychiatric conditions (6). The elevated levels of RRBs characteristic of ASD are
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impairing; the intensity and inflexibility associated with these repetitive actions and limited
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interests constrains opportunities to access environmental input important for learning and social
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development (7). Additionally, RRBs in both children and adults have been linked to alterations
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likely to broadly affect task performance, including abnormal sensory processing and deficits in
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cognitive control and executive functioning (8-14). Neural correlates of ASD include atypical
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local and network-level brain structure and resting-state functional networks from early
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development through adulthood (14-30). Elucidating the relationships between RRBs and the
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maturation of the brain’s functional networks during typical and atypical early development may
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enhance early risk assessment, inform developmental models of ASD pathogenesis, and provide
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a neurophysiological foundation for novel interventions focused on RRBs.
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The RRB domain encompasses a heterogeneous set of behaviors—intense
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preoccupations, stereotyped movements, and resistance to change (5)—that may be better
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conceptualized as distinct subcategories (7, 31, 32). Factor analytic studies in both young
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children and adults support the existence of considerable structure within the RRB domain,
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where a five-factor model both provides superior precision in RRB behavioral profiling (33, 34)
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and highlights developmental trajectories of unique RRB subcategories evident as early as 12
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months (4, 31). However, characterizing the specific neural contributions to the emergence of
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these behaviors has proven challenging (35-38), due in part to the significant methodological
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challenges of imaging brain function in infants and toddlers. Most studies using either task-based
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functional MRI (fMRI) or resting-state functional connectivity MRI (fcMRI) have focused
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largely on adulthood, rather than the first years of life when such behaviors begin to emerge.
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Here, we aimed to characterize the relationship between subcategories of RRBs and functional connectivity (fc) within and between putative brain networks over a pivotal time in
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development for both ASD-specific behavioral features (3, 4) and functional network
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organization (39-42). To quantify RRBs across four subcategories in a sample of 12-month old
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infants and 24-month old toddlers at high and low familial risk for ASD, we used the Repetitive
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Behavior Scale-Revised (RBS-R) (32). In the same infants, brain functional network architecture
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was measured using resting-state fcMRI. We took a data-driven approach to assessing brain-
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behavior relationships because, as both RRBs and brain function exhibit significant maturation
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throughout early childhood, findings from older individuals may not extend to infants and
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toddlers. Functional connections most associated with RBS-R subcategory factor scores were
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identified using generalized linear modeling. We then used enrichment analyses, adapted from
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large-scale genome-wide association studies, to identify network pairs exhibiting a significantly
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increased density of these strong brain-behavior relationships (43, 44). This established
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statistical method facilitated a brain-wide approach while constraining the burden of multiple
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comparisons. We hypothesized that: (1) the functional connections most strongly associated with
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RRBs would involve the default mode network, the frontoparietal control network, the salience
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network, and striatal regions of the subcortical network (38, 45) – networks previously
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implicated with RRBs in studies on older participants, and (2) RBS-R subcategories previously
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combined into an alternate behavioral category (33, 34) would be associated with overlapping
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patterns of functional network pairs.
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74 Materials and Methods
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Participants
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Participants were recruited as part of an ongoing, multisite, Autism Centers of Excellence
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Network study — the Infant Brain Imaging Study (IBIS), which has collected a large infant
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sibling sample with prospective brain-behavioral data across a crucial time window during which
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ASD develops. Individuals were assessed and scanned at each of four clinical sites: University of
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North Carolina, University of Washington, The Children’s Hospital of Philadelphia, and
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Washington University in St. Louis. The research protocol was approved by the institutional
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review boards at all clinical sites, and parents provided written informed consent after receiving
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a detailed description of the study.
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Infants were classified as high-risk (HR) if they had at least one sibling with a diagnosis
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of ASD, or low-risk (LR) if they had at least one typically developing older sibling and no first
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or second degree relatives with ASD or an intellectual disability. Participants were subsequently
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assigned diagnostic outcome labels: HR-ASD-positive, HR-ASD-negative, or LR-ASD-negative,
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depending on clinical best estimate, applying the DSM-IV-TR (5) checklist to all available data
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at 24 months. LR-ASD-positive subjects were omitted from the analyses (Table 1). Because HR
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children show elevated occurrence rates of RRBs relative to LR controls (4), this sample
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provided a sufficient range of RBS-R scores to adequately detect relationships between
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behavioral scores and dimensional metrics of brain function. All included participants
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contributed both behavioral and fcMRI data at visits corresponding to 12 and/or 24 months of
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age (range: 11.1-15.0 months and 22.4-27.0 months, respectively). All datasets were subject to
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stringent fcMRI quality control criteria and IBIS behavioral and structural MRI inclusion criteria
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(21) (see the Supplement for details). Age groups did not significantly differ by proportion of
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children later diagnosed with ASD (Fisher’s exact test, p=0.36), HR participants (p=1.0), girls
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(p=0.47), or cognitive development (Mullen composite standard score, Welch’s t-test, p=0.24).
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100 Behavioral assessment
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RRBs were assessed using the RBS-R (32), a parent/caregiver rated questionnaire consisting of
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43 items that has been validated for use among toddlers and preschool age children (4, 33, 34).
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Responses focused on the counts of items-endorsed rather than on severity scores, as the latter
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may be more susceptible to rater bias (4). The 43 items have been conceptually grouped into six
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subscales: stereotyped behavior, self-injurious behavior, compulsive behavior, ritualistic
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behavior, sameness behavior, and restricted behavior (32). In line with previous factor analytic
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studies (33, 34), ritualistic and sameness subtests were combined into a single factor. The
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compulsive factor was excluded as this subscale was not developmentally appropriate for the
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subjects included in the current study (4). The present study focused on the remaining four
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factors: restricted, stereotyped, ritualistic/sameness, and self-injurious behaviors (Figure 1A-D;
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Supplemental Table S1). There was no effect of site on RRB scores, and age-in-months was not
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correlated with RRB score (Supplement; Supplemental Table S2).
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Imaging acquisition
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Anatomical and functional brain imaging was carried out at all clinical sites using identical,
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cross-site calibrated 3-T Siemens TIM trio scanners (Siemens Medical Solutions, Malvern, PA),
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each equipped with standard 12-channel head coils. All infants were scanned during natural sleep
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(43). Each of two to three acquisitions was comprised of 130 temporally contiguous frames
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spanning 5.4 minutes. See the Supplement for details.
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fMRI preprocessing and fidelity optimization
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Data were preprocessed to reduce artifacts (i.e. BOLD signal changes not resulting from neural
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activity) and spatially registered to a 3 mm isotropic space using previously outlined procedures
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(41, 46). Small degrees of head motion-induced artifact can significantly alter correlations in
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resting-state data and confound interpretations of functional connectivity, particularly in studies
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of development where age is a factor of interest (47-49). To optimize data fidelity and minimize
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artifact, stringent thresholds in motion censoring “scrubbing” based on frame-to-frame
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displacement (FD; calculated as the sum of the absolute values of the six different realignment
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estimates—X, Y, Z, pitch, yaw, roll—at every time point (50)), number of contiguous frames,
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and total frame number were maintained. Frames with calculated FD ≥ 0.2 mm were marked for
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censoring. See the Supplement for further details. Exactly 150 frames, corresponding to 6.25
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minutes, of high-quality, low-motion MRI data were used from each participant within each age
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group.
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fcMRI preprocessing
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Following previously described procedures (50), data were voxel-wise demeaned and detrended
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within runs, while censored frames were ignored. Nuisance waveforms (including the global
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signal) were regressed voxelwise from the data, ignoring censored frames (Supplement). In
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frames marked for censoring, data were replaced by interpolated values computed by least-
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squares spectral analysis (50, 51). Interpolated data were only included for bandpass filtering and
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did not factor into correlation values. Finally, the data were spatially smoothed using a Gaussian
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kernel (6 mm FWHM isotropic).
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144 Definition of ROIs and functional connectivity computation
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The derivation of 230 regions of interest for studies on this population was previously described
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(41, 43) (Supplemental Figure S1). ROI-specific timecourses were calculated by averaging the
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timecourses of all voxels contained within 10 mm diameter spheres. Functional connectivity (fc)
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values were calculated as the pairwise zero-lag Pearson correlation between each of the 26,335
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pairs of ROI timecourses, and then Fisher-z transformed.
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Derivation of putative functional networks in infants and toddlers
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We utilized a previously described cross-age functional brain network model composed of these
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230 ROIs from a longitudinal cohort of N=48 children with clean fcMRI data at both 12-and 24-
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months (43) (see the Supplement for details). This model of the combined infant-toddler
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functional brain networks included 13 putative networks with naming informed by published
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adult networks (47): visual (Vis), temporal default mode network (tDMN), posterior cingulate
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DMN (pcDMN), anterior DMN (aDMN), somato-motor network (SMN), somato-motor network
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2 (SMN2), dorsal attention network (DAN), posterior frontal parietal control network (pFPC),
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anterior FPC (aFPC), subcortex (SubCtx), cingulo-opercular (CO), posterior CO (pCO), and
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salience (Sal) (Figure 1; Supplemental Figure S1) (43, 52). To generate complementary
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analyses using adult functional networks, we adapted a functional brain network structure
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derived from a published independent fcMRI dataset of typical adults (53).
164 Statistical Analysis
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Differences in RRB subcategory scores across age were calculated across the larger IBIS sample
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of participants who contributed behavioral data (N = 467 at 12 months, N = 379 at 24 months,
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where n = 327 had data at both time points) with the Wilcoxon signed-rank test (alpha level of
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0.0125 reflecting Bonferroni correction for the four behaviors tested). Using the matched fcMRI-
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RBS-R data, generalized linear modeling was used to examine the predictive relationship
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between brain fc and RRB subcategory scores separately for each of the 26,335 ROI pairs, at
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each time point, yielding two 26,335-element matrices of regression coefficients for each RRB
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subcategory (e.g. Figure 2A). Specifically, we fit a fixed-effect negative binomial regression
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with the relevant RBS-R subcategory factor as outcome and the ROI-ROI fc values as predictors.
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The negative binomial distribution provided optimal modeling for the RBS-R inventories that
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contained heavily zero-weighted integer count data (Figure 1 A-D; (14)), based on goodness-of-
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fit statistics that indicated superiority to a Poisson distribution model. Enrichment analyses were
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used to determine whether strong (defined as p ≤ 0.05, uncorrected; e.g., Figure 2B) brain-
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behavior relationship values (i.e., regression coefficients between RBS and fc for each ROI-pair)
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were significantly clustered within specific network pairs (43) (see the Supplement for details).
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A McNemar test was performed to determine whether patterns of brain-behavior relationships
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significantly differed between the 12- and 24- month groups. Stringent, brain-wide empirical
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significance levels—reflecting the 1.25% false positive rate—were determined using
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randomization (43). An alpha of 0.0125 was used to account for the four behavioral factors
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analyzed. The randomization procedure preserved the correlation and missing-data patterns in
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the data (e.g., infants assessed at 12 months only) and produced two separate brain-wide null
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distributions for statistical evaluation of the data—one for each time point (Supplement).
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Primary results include network pairs either significantly enriched at both time points, or
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significantly enriched at one age and significantly different in their set of brain-behavior
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relationships between age groups (e.g. Figure 2D). In contrast, network pairs that exhibit
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significant enrichment at one time point –but do not differ statistically from null results at the
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other time –are presented as discovery findings that may provide potential targets for future
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hypothesis-driven studies.
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Results
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Behavioral characterization
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We analyzed behavioral and neuroimaging data collected from infants at 12 (n=118) and/or 24
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months of age (n=87; n=38 children provided data at both ages) (Table 1). Restricted interests,
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stereotyped mannerisms, and self-injurious behavior scores did not show significant differences
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across age (restricted p=0.77; stereotyped p=0.72; self-injurious=0.61), whereas
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ritualistic/sameness behavior showed a significant age-related increase (p=1.8x10-4; consistent
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with previous findings (4)).
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Restricted behavior associations with functional connectivity
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The primary findings in relation to restricted behavior include only two of the possible 91 total
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network pairs: the tDMN-DAN and tDMN-pFPC at 24 mo (Figure 2). Each network pair
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exhibited significant clustering of positive brain-behavior relationships (Figure 2D), meaning 11
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that more positive fc values between these brain regions were associated with higher restricted
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behavior scores. Though the associations between fcMRI and restricted scores were positive
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within these two network pairs, the range of the fcMRI values, themselves, within the tDMN-
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DAN and tDMN-pFPC were remarkably distinct (Supplemental Figure S2). Specifically, in
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77% of the tDMN-DAN ROI pairs, predominantly negative fc values were exhibited. The
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observation of a positive relationship between fc and restricted behavior in the context of
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predominantly negative fc for ROI pairs indicates that strongly negative tDMN-DAN fc is
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associated with fewer restricted behaviors. In contrast, 70% of tDMN-pFPC connections
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contributing to enrichment showed predominantly positive fc values. In addition to these primary
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findings, two discovery-level findings were also observed at 12 mo: negative fc-restricted
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relationships in Vis-tDMN and positive fc-restricted relationships in Vis-SMN2.
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Stereotyped behavior associations with functional connectivity
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Primary findings include three network pairs: Vis-tDMN at 12 months, as well as DAN-SubCtx
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and tDMN-pFPC at 24 months (Figure 3). At 12 months, Vis-tDMN connections showed
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significant clustering of strong negative brain-behavior relationships (Figure 3D), with 77%
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exhibiting predominantly negative fc (Supplemental Figure S3). In contrast, at 24 months,
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greater stereotyped scores were associated with more positive fc between tDMN-pFPC and
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DAN-SubCtx (Figure 3). Of tDMN-pFPC connections contributing enrichment, 83% showed
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primarily positive fc, while 89% of implicated DAN-SubCtx ROI pairs showed primarily
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negative fc values (Supplemental Figure S3). Of the subcortical ROIs contributing to
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enrichment, four ROIs, all located in the putamen/lentiform nucleus, were implicated markedly
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more frequently than the others (MNI coordinates: -31.4, -11.5, -0.3; 30.5, -13.9, 1.7; 23.3, 10.2,
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1.5; 28.5, 0.8, 4.0). Discovery findings include three network pairs at 12 months (Figure 3C),
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including enrichment of positive fc-stereotyped relationships within the visual network (Vis-Vis)
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and negative fc-stereotyped relationships within the Vis-pFPC and pcDMN-pFPC (Figure 3B).
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Ritualistic/sameness behavior associations with functional connectivity
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Primary findings include higher ritualistic/sameness scores associated with less positive fc
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between Vis-tDMN at 12 months (with 82% of connections showing primarily negative fc)
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(Figure 4; Supplemental Figure S4), as well as less positive fc between Vis-pFPC at both 12
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and 24 months (with primarily negative fc in 85% of connections at 12 months and 90% of
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connections at 24 months). Of the implicated Vis-pFPC connections, 16% (9/55) of connections
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at 12 months and 23% (9/39) of connections at 24 months were implicated at both time points.
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Discovery findings included four additional network pairs: enrichment of positive fc-ritualistic
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relationships within Vis-Vis and Vis-pcDMN at 12 months, enrichment of negative fc-ritualistic
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relationships within tDMN-pcDMN at 12 months, and enrichment of positive fc-ritualistic
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relationships between the tDMN-pFPC at 24 months (Figure 4).
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Self-injurious behavior associations with functional connectivity
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No primary level findings were observed in the fc-self-injurious relationships at either age
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(Supplemental Figure S5). A discovery level finding was observed at 24 months within Vis-
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DAN.
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Convergence across RRB subcategories
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As detailed above, Vis-tDMN was associated with both stereotyped and ritualistic/sameness
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behaviors at 12 months, and at 24 months tDMN-pFPC was associated with both stereotyped and
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restricted behaviors (Figure 5 for summary of results). In each case, convergence at the network
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level was also represented at the level of individual ROI connections (Figure 6). Present in the
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convergent findings at both ages are tDMN ROIs predominantly distributed around the lateral
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right temporal lobe and the right temporal-parietal junction.
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Discussion
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Though exhibitions of RRBs reflect crucial stages of early typical development, accumulating
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evidence suggests that early elevation of these behaviors may provide a potent early risk marker
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for later diagnosis of ASD. Our primary findings (summarized in Figure 5) reveal that: (1)
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neural signatures of RRB subcategories are distinct between 12 and 24 months of age, with the
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single exception of negative Vis-pFPC fc associations with ritualistic/sameness behavior (Figure
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4); (2) at 12 months of age, fc between Vis-tDMN is significantly associated with both
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ritualistic/sameness and stereotyped behaviors (Figures 3,4,6); (3) at 24 months of age, tDMN-
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pFPC fc is significantly associated with both stereotyped and restricted behaviors (Figures
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2,3,6); (4) at 24 months of age, fc between tDMN-DAN is significantly associated with restricted
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behavior (Figure 2); and (5) at 24 months of age, fc between DAN-SubCtx is significantly
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associated with stereotyped behavior (Figure 3).
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First, our results show that higher scores for restricted behavior—defined by a limited
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range of focus, interest, or activity—are associated with reduced anti-phase correlations (less
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negative fc) between the tDMN and the DAN, as well as more positive fc between tDMN-pFPC.
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Interactions between regions within the DAN, the default mode network (DMN), and the fronto-
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parietal control (FPC) network, have been shown to shift connectivity and network affiliation in
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a dynamic, flexible, and adaptive manner to support specific task demands (45, 54-61). Increased
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exhibition of RRBs has been shown to be associated with task-evoked and resting-state fc
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patterns of networks including the DMN and central executive networks (corresponding to our
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FPC (45)). An anti-correlated relationship between the DAN and DMN is well documented in
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both task and resting-state fMRI studies in adults (62-64), with weaker anti-correlations
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predicting greater impairment in attention and inhibitory control (65), and providing a possible
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mechanism for limited cognitive flexibility. Further, resting-state anti-correlated patterns
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between DAN and DMN have been observed to emerge over the first year of life and become
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increasingly anti-correlated by 24 months (66). Our results add empirical support to the
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hypothesis that inverse functional relationships between DAN and DMN regions reflect healthy
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typical development, and that abnormal network dynamics between the tDMN, DAN, and pFPC
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may underlie rigid and repetitive behaviors and interests (45, 55, 66-69).
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The stereotyped behavior factor, which includes apparently purposeless movements repeated in a similar manner, was associated with more positive fc between tDMN-pFPC and
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DAN-SubCtx at 24 months, as well as decreasing fc between Vis-tDMN at 12 months. The
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convergent findings between restricted and stereotyped behaviors (tDMN-pFPC) are consistent
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with factor analytic studies that grouped items from these two behaviors into a single factor (33,
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34). Additionally, we observed that DAN connectivity with subcortical regions, primarily the
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putamen/lentiform nucleus and cerebellum, was uniquely associated with stereotyped behavior.
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These findings provide further empirical support for significant involvement of cortico-striatal-
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thalamo-cortical circuitry in stereotypies (70-72). Further, atypical recruitment and regulation of
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visual cortical regions and atypical visual attention have been linked with RRB symptomology
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(73-75) and abnormalities in the control of visual attention have been shown to persist
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throughout infancy in children who are later diagnosed with ASD (23, 76-78).
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The ritualistic/sameness behavior factor, which contains items related to visual preference (e.g., likes same movie/scene played continuously), was associated with less positive
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fc between Vis-tDMN (79, 80) at 12 months, as well as between Vis-pFPC at both 12 and 24
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months. Studies in older children have shown similar associations between greater ASD
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symptomology and reduced resting-state fc between the visual network and other large-scale
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functional networks including motor and salience networks (74, 81). Given previous studies that
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have linked RRBs to atypical sensory response patterns including hyper responsivity to sensory
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stimuli and sensory seeking behavior (9-11, 14), our findings support the hypothesis that early
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disordered fc involving the visual network may engender later disruptions in higher order
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behaviors (82, 83).
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The convergence at 12 months between stereotyped and ritualistic/sameness behaviors
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(Vis-tDMN) was unexpected, as different RBS-R factor schemes categorize these two behaviors
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as lower vs higher order, respectively (34, 84, 85). Although these behaviors show distinct
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developmental trajectories and outward manifestations, these are the two RBS-R factors that
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show the most robust associations with atypical sensory features (10, 11). The highly
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overlapping involvement of Vis-tDMN suggests that the two behavioral subcategories may share
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a common etiology.
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Our convergent findings across RRB subcategories highlight connectivity between
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temporal regions of the infant DMN with Vis and pFPC networks. Of the 22 tDMN ROIs, 17
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were located along either side of the superior temporal sulcus (STS; as were a number of
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implicated pFPC ROIs). Both Vis-tDMN and tDMN-pFPC included a subgroup of tDMN ROIs
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located in the right posterior STS (pSTS) and nearby temporal parietal junction region. The right
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pSTS has been implicated in social functions affected in ASD, including processing biological
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motion and eye gaze (86), as well as basic perceptual functions (e.g., audiovisual integration)
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(87-91) and the control of visual attention (92). Decreased activation of the STS during
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visuomotor learning in individuals with ASD has been linked to more severe RRBs (93). Thus,
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the observed RRB-fc relationships involving the STS, particularly right STS, by 12 months of
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age, support common neurophysiological bases for the development of atypical social and RRB
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behaviors and the presentation of ASD as a clinical syndrome. This overlap in neural substrates
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is also consistent with prior behavioral studies showing that both social and RRB features load
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onto a primary factor underlying the continuum of autistic traits (94).
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Several limitations and future directions merit discussion. First, because we included participants with longitudinal and cross-sectional data in order to maximize sample size at each
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time point, cross-age comparisons involve some non-overlapping participants. Future studies
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utilizing exclusively longitudinal data to elucidate within-subject trajectories will be required to
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better understand how these networks instantiate behavior across development. Second,
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differences may exist between high- and low-risk groups both within and across age groups in
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brain-behavior relationships, and the network architecture underlying RRB subcategories may
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differ in children who develop ASD. However, our study was not designed or powered to
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address these questions; rather, this sample provided the range of behavior required to adequately
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elucidate the observed brain-RRB relationships. These behaviors are continuously distributed,
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and by capitalizing on the variance afforded by this mixed group we maximized our chances of
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identifying important relationships for this initial study of fcMRI correlates of RRBs in the first
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two years of life. This work sets the stage for future studies comparing brain-behavior
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relationships in larger age-specific, risk, and outcome sub-groups. Third, though our set of
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subcortical and cerebellar ROIs was not comprehensive, the functionally-defined ROIs in the
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current study are well vetted (41), and recent related work demonstrated the utility and validity
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of enrichment analyses using these ROIs as a novel method for analyzing brain-behavior
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relationships (43, 52). Fourth, our fcMRI processing included global signal regression (GSR), an
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established approach to removing motion-induced artifacts from fcMRI data (50, 95). Recent
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studies have established that GSR in combination with volume censoring is superior to other
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denoising procedures for the removal of motion artifact (96), though a limitation of this approach
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is that current methods cannot rule out the concomitant removal of genuine neural signal. Given
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that motion-related artifacts present a major confound in fc analyses (47, 49, 95, 97), our
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approach conservatively accounted for motion-related artifacts to minimize the risk of spurious
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interpretations of fc-behavior relationships. Finally, differences between the infant/toddler and
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adult networks may be attributed to age-related development, but may also reflect
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methodological differences due to possible changes in functional network organization during
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sleep (98-100).
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The implicated network pairs for RRB subcategories were different from those
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implicated for initiation of joint attention (43) and also for walking and gross motor behaviors
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(52). These non-overlapping findings support the specificity of the brain-behavior relationships
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revealed with our fcMRI enrichment approach. Future studies that systematically analyze the
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overlap between networks enriched for specific RRB subcategories and other early emerging
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behavioral features associated with ASD, particularly atypical sensory response patterns and
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motor development, could provide further insight into the complex relationship between
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dimensional aspects of behavior and underlying neurobiology in typical and atypical
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development. Comparing the trajectories of these early developmental brain-behavior
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relationships across different outcome groups would offer the potential for future identification
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of predictive biomarkers to aid in distinguishing typical and atypical early development.
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Furthermore, given the paucity of intervention practices targeted toward RRBs in ASD (101),
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changes in associated brain fc may be useful in tracking the efficacy of novel interventions.
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Acknowledgments and Disclosures We wish to thank the families and children for their time and participation. We thank Sarah Hoertel for key early analyses and for her help with guiding the brain-behavior analysis strategy.
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We also thank Leigh MacIntyre for managing the full IBIS database.
This work was supported by grants from the National Institutes of Health, K01 MH103594
(Eggebrecht), R01 MH093510 (Pruett), an NIH Autism Centers of Excellence Network grant,
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R01 HD055741 (Piven), Autism Speaks #6020 (Piven), the Simons Foundation #140209 (Piven), K01 MH101653 (Wolff), P30 NS098577 (Snyder), U54 Intellectual and Developmental
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Disabilities Research Centers (IDDRC; HD079124 to UNC (Piven), HD087011 to Washington University (Constantino and Schlaggar), HD86984 to CHOP (Schultz), HD083091 to UW (Estes)), and the McDonnell Center for Systems Neuroscience. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National
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Institutes of Health.
Competing interests: The authors declare the following conflicts of interests: R.C.M. receives acting, modeling, and speaking fees from Siemens Healthcare; A.C.E. is a founder and a member
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of the Board of Directors of Biospective Inc. All other authors declare no biomedical financial interests or potential conflicts of interest.
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Author contributions: Conceptualization, J.R.P. , J.J.W, J.T.E.., and J.P.; Data Curation, J.T.E., J.J.W., A.M.E., L.Z., C.M.A., A.Z.S, K.N.B., M.A.S., and G.G.; Formal Analysis, C.J.M., A.T.E. and A.T.; Investigation, C.J.M., A.T.E., J.J.W., J.T.E., A.T., and J.R.P.; Methodology, C.J.M., A.T.E, S.E.P., A.T., J.R.P., and B.L.S.; Project Administration, J.R.P., J.T.E., J.P., K.N.B., L.Z., A.C.E., H.C.H., S.J.P., R.T.S., A.M.E., and M.A.S.; Resources, K.N.B., A.C.E., H.C.H., S.J.P., R.T.S., J.P., and J.R.P.; Software, C.M. A.T.E., A.T., and J.R.P.; Supervision,
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K.N.B., A.C.E., H.C.H., S.J.P., R.T.S., J.P., S.E.P., and J.R.P.; Visualization, C.J.M., and A.T.E.; Writing – Original Draft, C.J.M., A.T.E., J.T.E., J.R.P.; Writing – Review and Editing, C.J.M., A.T.E., J.T.E., A.T., J.J.W., C.M.A., A.M.E., L.Z., K.N.B., R.C.M., N.M., H.C.H.,
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S.J.P., R.T.S., M.A.S., G.G., B.L.S., S.E.P., J.P., and J.R.P.; Funding Acquisition, J.P., J.R.P., A.T.E., and J.J.W.
∆ The Infant Brain Imaging Study (IBIS) Network is an NIH funded Autism Centers of
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Excellence project and consists of a consortium of eight universities in the U.S. and Canada.
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Clinical Sites: University of North Carolina: J. Piven (IBIS Network PI), H.C. Hazlett, C. Chappell; University of Washington: S. Dager, A. Estes, D. Shaw; Washington University: K. Botteron, R. McKinstry, J. Constantino, J. Pruett; Children’s Hospital of Philadelphia: R. Schultz, J. Pandey; University of Alberta: L. Zwaigenbaum; University of Minnesota: J. Elison; Data Coordinating Center: Montreal Neurological Institute: A.C. Evans, D.L. Collins, G.B. Pike,
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V. Fonov, P. Kostopoulos; S. Das; Image Processing Core: New York University: G. Gerig; University of North Carolina: M. Styner; Statistical Analysis Core: University of North Carolina:
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H. Gu.
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79. Jones W, Carr K, Klin A (2008): Absence of preferential looking to the eyes of approaching adults predicts level of social disability in 2-year-old toddlers with autism spectrum disorder. Arch Gen Psychiatry. 65:946-954. 80. Jones W, 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. 81. Nebel MB, Eloyan A, Nettles CA, Sweeney KL, Ament K, Ward RE, et al. (2016): Intrinsic VisualMotor Synchrony Correlates With Social Deficits in Autism. Biol Psychiatry. 79:633-641. 82. Orekhova EV, Stroganova TA (2014): Arousal and attention re-orienting in autism spectrum disorders: evidence from auditory event-related potentials. Front Hum Neurosci. 8:34. 83. Thye MD, Bednarz HM, Herringshaw AJ, Sartin EB, Kana RK (2017): The impact of atypical sensory processing on social impairments in autism spectrum disorder. Dev Cogn Neurosci. 84. Mooney EL, Gray KM, Tonge BJ, Sweeney DJ, Taffe JR (2009): Factor analytic study of repetitive behaviours in young children with Pervasive Developmental Disorders. J Autism Dev Disord. 39:765-774. 85. Bishop SL, Hus V, Duncan A, Huerta M, Gotham K, Pickles A, et al. (2013): Subcategories of restricted and repetitive behaviors in children with autism spectrum disorders. J Autism Dev Disord. 43:1287-1297. 86. Pelphrey KA, Morris JP, McCarthy G (2005): Neural basis of eye gaze processing deficits in autism. Brain. 128:1038-1048. 87. Calvert GA (2001): Crossmodal processing in the human brain: insights from functional neuroimaging studies. Cereb Cortex. 11:1110-1123. 88. Beauchamp MS, Lee KE, Argall BD, Martin A (2004): Integration of auditory and visual information about objects in superior temporal sulcus. Neuron. 41:809-823. 89. Carrington SJ, Bailey AJ (2009): Are there theory of mind regions in the brain? A review of the neuroimaging literature. Hum Brain Mapp. 30:2313-2335. 90. Redcay E (2008): The superior temporal sulcus performs a common function for social and speech perception: implications for the emergence of autism. Neurosci Biobehav Rev. 32:123-142. 91. Taylor KI, Moss HE, Stamatakis EA, Tyler LK (2006): Binding crossmodal object features in perirhinal cortex. Proc Natl Acad Sci U S A. 103:8239-8244. 92. Corbetta M, Patel G, Shulman GL (2008): The reorienting system of the human brain: from environment to theory of mind. Neuron. 58:306-324. 93. Travers BG, Kana RK, Klinger LG, Klein CL, Klinger MR (2015): Motor learning in individuals with autism spectrum disorder: activation in superior parietal lobule related to learning and repetitive behaviors. Autism Res. 8:38-51. 94. Constantino JN, Gruber CP, Davis S, Hayes S, Passanante N, Przybeck T (2004): The factor structure of autistic traits. J Child Psychol Psychiatry. 45:719-726. 95. Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, et al. (2013): A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage. 76:183-201. 96. Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, Ruparel K, et al. (2017): Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage. 154:174-187. 97. Tyszka JM, Kennedy DP, Paul LK, Adolphs R (2014): Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism. Cereb Cortex. 24:1894-1905. 98. Spoormaker VI, Schroter MS, Gleiser PM, Andrade KC, Dresler M, Wehrle R, et al. (2010): Development of a large-scale functional brain network during human non-rapid eye movement sleep. J Neurosci. 30:11379-11387.
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99. Graham AM, Pfeifer JH, Fisher PA, Lin W, Gao W, Fair DA (2015): The potential of infant fMRI research and the study of early life stress as a promising exemplar. Developmental Cognitive Neuroscience. 12:12-39. 100. Mitra A, Snyder AZ, Tagliazucchi E, Laufs H, Elison J, Emerson RW, et al. (2017): Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness. PLoS ONE. 12:e0188122. 101. Boyd BA, McDonough SG, Bodfish JW (2012): Evidence-based behavioral interventions for repetitive behaviors in autism. J Autism Dev Disord. 42:1236-1248.
Figure Legends
Figure 1. Restricted and repetitive behaviors and functional connectivity in infants
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The number of items endorsed for each RBS-R factor at both 12 months old (mo; blue) and 24 mo (red): (A) the restricted behavior factor includes four items pertaining to limited range of focus, interest, or activity (e.g., preoccupation with part of object); (B) the stereotyped behavior factor includes six items relating to repeated, purposeless movements (e.g., arm flapping); (C) the self-injurious factor includes eight items relating to repeated actions that can cause injury to
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the body (e.g., hair pulling); (D) the ritualistic/sameness factor includes seventeen items relating to performing activities of daily living in a similar manner or resistance to change (e.g., arranging/ordering). Also see Supplemental Table S1. (E) An Infomap-sorted mean fcMRI matrix derived from the correlation structure between 230 functionally-defined regions of
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interest (ROIs). See Supplemental Figure S1. (F) Left lateral view of the ROIs on the brain surface, colored according to network assignment (see Materials and Methods for details and definition of network abbreviations). For clarity, ROIs in the cerebellum are displayed without
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the cerebellar structure.
Figure 2. Connections showing a strong relationship to restricted behavior are concentrated in temporal default mode—dorsal attention and temporal default mode— posterior frontoparietal control network pairs at 24 months. (A) Strong positive and negative brain-behavior relationships cluster by sign, within a subset of network blocks (note the visually striking red clustering at 24 mo but not at 12 mo indicated by the arrow). (B) Functional connections showing a strong relationship to restricted behavior are defined as those with p ≤ 27
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0.05. Quantifying the level of clustering with enrichment analyses (see Materials and Methods and Supplemental Figure S2) reveals that strong brain-behavior relationships are constrained to a minority of network pairs that differ across age (enriched network pairs outlined in black). (C) Only two functional network pairs significantly enriched at either age (
12 mo;
24 mo) also
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exhibit significant differences across age groups ( ● ; tested via McNemar χ2): tDMN-DAN and tDMN-pFPC, (see Supplemental Table S3). (D) For each primary result, ROI pairs contributing to enrichment are visualized on a surface representation of the cortex. Ball color denotes
functional network membership and line color joining ROI pairs denotes the sign of brain-
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behavior relationship (red-positive; blue-negative). The signs of brain-behavior relationships are largely consistent within network pairs. See Supplemental Figure S2 for more detailed analysis
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of the fc underlying these brain-behavior relationships.
Figure 3. Strong fc-stereotyped relationships cluster within temporal default mode—visual network connections at 12 months and dorsal attention—subcortex and temporal default mode—posterior frontoparietal control network connections at 24 months. Analyses are as outlined in Figure 2. (A) Negative binomial regression based relationships between fc and
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stereotyped behavior scores. (B) Significant clustering of strong brain-behavior associations (p ≤ 0.05) is restricted to a subset of network pairs (outlined in black). (C) Primary findings include tDMN-Vis enrichment at 12 mo (but not 24 mo) and DAN-SubCtx and tDMN-pFPC enrichment at 24 mo (but not 12 mo;). See Supplemental Figure S3 for enrichment and McNemar (age
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group comparison) analyses. See Supplemental Table S3 for statistics. (D) At 12 mo, the VistDMN network pair showed primarily negative fc-stereotyped relationships while at 24 mo both
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network pairs showed primarily positive fc-stereotyped relationships. See Supplemental Figure S3 for more detailed analysis of the fc underlying these brain-behavior relationships.
Figure 4. Strong fc-ritualistic/sameness relationships cluster within visual-temporal default mode network connections at 12 months and visual—posterior frontoparietal control network connections at 12 and 24 months. Analyses follow those outlined in Figure 2. (A) Note the particularly visually striking cluster of negative fc-ritualistic/sameness behavior relationships in the Vis-tDMN network pair at 12 mo. (B) Significant clustering of strong brainbehavior associations (p ≤ 0.05) is restricted to a subset of network pairs. (C) Primary findings 28
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included tDMN-Vis enrichment at 12 mo (but not 24 mo; indicated with dot in a blue box) and Vis-pFPC pair enrichment at both 12 mo and 24 mo (indicated with a green box). See Supplemental Figure S4 for McNemar (age group differences) analysis and Supplemental Table S3 for statistics. (D) Both Vis-tDMN (at 12 mo) and Vis-pFPC (at 12 mo and 24 mo)
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showed primarily negative fc-stereotyped relationships: higher ritualistic and sameness scores are associated with less positive fc between Vis and both tDMN and pFPC. See Supplemental Figure S4 for more detailed analysis of the fc underlying these brain-behavior relationships.
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Figure 5. Global patterns of RRB-fc relationships across age and behavior. The first column depicts the nature of the brain-behavior relationship, that is, whether the relationship is positive
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or negative and whether it spans primarily positive or negative fc values. The second column contains the network pair in question. The third column emphasizes whether greater behavior scores were associated with increasing or decreasing fc between the network pair. The last three columns catalogue implicated behavior and time point combinations for the given network pairs. See Supplemental Figure S5 for more detailed analysis of the self-injurious RBS-R subcategory. See Supplemental Figure S6 for the global RRB-fc relationships as derived using
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adult-based functional networks.
Figure 6. ROIs contributing to enrichment across behavior factors. Analyses revealed two network pairs, one at 12 mo and one at 24 mo, implicated with multiple behaviors. (A)
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Functional connections between Vis and tDMN that showed a strong relationship to both stereotyped and ritualistic/sameness behavior at 12 mo. (B) Functional connections between the
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pFPC-tDMN that showed a strong relationship to both restricted and stereotyped behavior at 24 mo are visualized on the brain. As above, the color of the ROI denotes the functional network, and the color of the connecting bar denotes the sign of the brain-behavior relationship.
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Tables: Table 1. Demographics 24-month age group
Number participants with data available at both time points (n)
467 135 118
379 107 87
327 48 38
31 76 11 74 12.5 (0.5) 98.0(18.2)
20 56 11 50 24.6 (0.6) 99.8(20.8)
12 24 2 22 -
0.3 (0.8)
0.4 (0.9)
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Stereotyped 0.6(1.2) Ritualistic + Sameness 0.6 (1.6) Self-Injurious 0.4 (0.8)
0.7 (1.3) 1.4 (1.9) 0.5 (1.1)
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RBS-R Low motion fcMRI Botha a Outcome group LRHRHR+ a Number of Males Age in monthsa Mean Mullen Early Learning Composite score (SD)a Mean RBS-R score (SD)a Restricted
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12-month age group
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Numbers reflect the group of participants providing both RBS-R and low motion fcMRI data RBS-R: Repetitive Behavior Scale-Revised fcMRI: functional connectivity: Magnetic Resonance Imaging LR-: Low-risk, negative ASD diagnosis HR-: High-risk, negative ASD diagnosis HR+: High-risk, positive ASD diagnosis SD: Standard deviation
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Restricted and Repetitive Behavior and Brain Functional Connectivity in Infants at Risk for Developing Autism Spectrum Disorder
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Supplemental Information Exclusion criteria for participants
Exclusion criteria included: known genetic conditions; gestational age < 36 weeks or birth weight
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< 2000 g; maternal substance abuse during pregnancy; contraindication for MRI; a first degree relative with psychosis, schizophrenia, or bipolar disorder; significant medical or neurological
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conditions affecting growth, development, or cognition; non-English-speaking family; adoption; or twin birth.
Effect of clinical assessment site and age in months on RBS-R score
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The potential effect of site on RBS-R scores was tested using a 1-way ANOVA. The potential effect of age on RRB score within each age group was examined with Spearman’s correlation. There was no effect of site on RRB scores, and age-in-months was not correlated with RRB score
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behaviors tested).
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(Supplemental Table S2, alpha level of 0.0125 reflecting Bonferroni correction for the four
Image acquisition
The protocol included T1-weighted and T2-weighted anatomical imaging, and resting-state fcMRI. The present study utilized the 3-D sagittal T2-weighted sequence (TE = 497 ms, TR = 3200 ms, matrix 256 x 256 x 160, 1 mm3 voxels) for anatomical segmentation, and spatial normalization procedures (1-3). Functional images were acquired using a gradient echo planar sequence, sensitive to changes in T2* BOLD signal (TE = 27 ms, TR = 2500 ms, voxel size 4 mm
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isotropic, flip angle 90 degrees, field of view 256 mm, matrix 64 x 64, pixel bandwidth 1,906 Hz). Atlas transformation of the functional data was computed through a sequence of affine transforms. First, T2-weighted images were registered to age-specific (12 and 24 months) MRI atlas targets to
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account for morphological changes across development (4). fMRI data were then aligned to the atlas registered T2-weighted images. After fMRI–to–T2-weighted atlas transform composition, the volumetric time series were resampled in atlas space (3 mm voxels) including correction for
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head movement in a single resampling step. Finally, each atlas-transformed functional dataset was
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visually inspected in sagittal, transverse, and coronal views to ensure proper spatial alignment.
fMRI preprocessing
Following previous work (1, 5), initial fMRI data preprocessing included: (i) sinc interpolation to compensate for slice dependent time shifts, (ii) correction of systematic odd-even slice intensity
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differences caused by interleaved acquisition, (iii) spatial realignment to account for head motion both within and across fMRI runs, and (iv) within-run intensity normalization to a whole brain mode value of 1000 (6). Data were then subjected to rigorous frame censoring (“scrubbing”) based
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on the frame-to-frame displacement (FD) measure (7), which sums 3 absolute translations (X, Y, Z) and 3 absolute rotations (Pitch, Yaw, Roll) evaluated at a radius of 50 mm. Frames with FD
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≥0.2 mm were marked for subsequent censoring. Temporally isolated (5 or fewer contiguous) FD <0.2 mm frames were also censored. fMRI runs with fewer than 30 uncensored frames were discarded. After frame censoring, epochs of data containing six or more contiguous uncensored frames were used for further analyses. The fMRI runs with fewer than 30 uncensored frames were discarded. No significant relationship was observed in the percentage of scrubbed volumes with regard to age (Mann-Whitney U-test: n=118, z=0.92, p=0.36). To control for potential biases
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attributable to the amount of data per cohort, 150 non-censored (retained) fMRI frames were used, prioritizing runs with the most retained frames, for correlation analysis in each subject.
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Definition of nuisance regressors and regions of “non-interest” in atlas space
The nuisance regressors included (i) three translation and three rotation time series derived by retrospective head motion correction, together with expansion terms (8), and (ii) time series
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derived from regions of non-interest (whole brain, white matter, and cerebrospinal fluid) and their 1st derivatives. White matter and CSF regions were manually defined in atlas-transformed T1-
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weighted images, from 15 representative subjects in each age group (including data from both high- and low-risk individuals). To reduce the risk of intruding on gray matter, these regions of noninterest were eroded away from boundaries with other tissues using a 2.5 mm Gaussian blurring kernel. The intersection over each age group was computed to create the final white matter and
Definition of ROIs
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CSF regions of noninterest.
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Briefly, 280 initial regions of interest (ROIs) were defined from a combination of meta-analyses of autism studies (9) and of task data and cortical functional areal parcellations (10) obtained in
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typical subjects (11). After ROI placements were inspected on structural anatomy registered to age-specific atlas templates, 50 ROIs were removed because they were partially located outside of the whole brain mask or showed inconsistent gray matter coverage across ages.
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Derivation of putative functional networks in infants and toddlers We generated a cross-age network model using the mean fcMRI matrix from a longitudinal cohort of N=48 children with clean fcMRI data at both 12-and 24-months (Supplemental Figure S1).
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Functional network derivation closely followed previously published procedures (12). The full set of ROI-pair correlations (Fisher-z values) was averaged across subjects producing a 230 x 230 connection matrix (Fig. 1E; nodes=ROIs, edges=correlation coefficients). The 230 ROIs were then
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separated into functional networks (Fig. 1F) using the Infomap community detection algorithm(13) that assigns ROIs to communities based on the maximization of within-module random walks in
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the adjacency matrix derived from the thresholded and binarized connection matrix. Infomap solutions were generated for each of 91 edge density thresholds (1-10% in steps of 0.1%). An automated consensus procedure generated a single model of the community structure, or equivalently network structure, by first combining Infomap solutions across edge density
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thresholds by maximizing the normalized mutual information of groups of neighboring solutions and then maximizing modularity (3). Only Infomap solutions for graphs with the largest component containing more than 95% of the nodes were included in the consensus procedure.
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Communities containing fewer than five ROIs were combined and defined as a collection denoted as unspecified ROIs (US). As we were interested in brain-behavior interactions at the systems
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network level, unspecified ROIs were omitted from the current analysis.
Enrichment analysis details These data-driven statistical methods, adapted from genome-wide association studies (14-16) were used to determine whether strong (defined as p ≤ 0.05, uncorrected; e.g., Figure 2B) brainbehavior relationship values (regression coefficients between RBS and fc for each ROI-pair) were
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significantly clustered within specific network pairs, rather than uniformly distributed throughout all network pairs. A 2-sided χ2(df=1) test and a complementary hypergeometric test (e.g., Supplemental Figure S2) were calculated on the number of observed strong (p ≤ 0.05) brain-
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behavior relationship values within each network pair, with the null hypothesis being that the number of strong values is uniformly distributed across all possible network pairs. High χ2 values indicate that the number of strong brain-behavior relationships within each network pair is
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enriched (or depleted), that is much greater than (or much less) than the number expected by chance, respectively. As we were only interested in those network pairs that showed enrichment,
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data-driven 1-tailed p-values were calculated from permutation testing, as described below, by setting the p-values of ROI pairs showing depletion to 1. To complement the χ2 statistics, the hypergeometric statistic was also calculated. This test assesses the likelihood of observing the given number of strong relationships in a network pair, given the total number of strong
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relationships observed, the total number of ROI-pairs, and the number of ROI-pairs within the network pair of interest. Enriched network pairs were those that met significance, as determined by permutation testing (see below), for both the χ2 and hypergeometric tests. The McNemar χ2
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For each of the χ2, hypergeometric, and McNemar tests, empirical significance levels—
reflecting the 5% false positive rate for enrichment—were determined using permutation testing where the fcMRI-RBS-R data pairing was randomized for each of 10,000 iterations. The set of permutation-based false statistics from all of the network blocks represented a brain-wide null distribution for statistical evaluation of the data. The relative locations of the true data statistics in these distributions provided the reported permutation-based false positive rate p-values and
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reflected the empirical probability of calculating the actual observed statistical value (χ2, hypergeometric, and McNemar) for any network pair in the permutation. In this way, permutation testing provided control over family wise error rate without the need to make assumptions about
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the shape or parameters of the underlying population distribution (14). The randomization preserved the correlation and missing data patterns in the data (e.g., infants assessed at 12 months only). At each iteration, permutations of complete fcMRI matrices were conducted separately on
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the 12-month only group, the 24-month only group, and for the set of data at both time points. Longitudinal subjects were then recombined with cross-sectional subjects producing two separate
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brain-wide null distributions — one for each time point.
Enrichment analyses using adult network structure
Complementary enrichment analyses were also performed using a brain functional network
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structure derived from a previously published independent fcMRI dataset of typical adults (11) (Supplemental Figure S1). While these analyses revealed highly similar patterns of network level brain-behavior relationships (see Supplemental Figure S6 for overall pattern), fewer network
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Supplemental Figure S1. Defining infant-toddler brain functional network structure. Related to Figure 1. (A) The 230 ROIs used in the current study, sorted according to a consensus Infomap model of infant/toddler network structure using fcMRI data from 48 children with fcMRI data at both the 12 mo and 24 mo time points (network abbreviations given in Materials and Methods). (B) Adapted model of adult functional networks (11).
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Supplemental Figure S2. Restricted Enrichment Details. Related to Figure 2. (A) Enrichment analyses were used to quantify the relative density of connections showing strong brain-behavior relationships (black dots indicate uncorrected negative binomial regression coefficient pvalues<=0.05) within specific network pairs. This was assessed using complementary (B) chisquare and (C) hypergeometric statistics. Network blocks are colored based on p-values via a permutation-based, experiment-wide, false positive rate. Significance is indicated with a dot and additionally reflects multiple comparison correction for four behaviors tested. (D) A McNemar test evaluated differences in brain-behavior associations between age groups. (E) tDMN-DAN and tDMN-pFPC showed both significant enrichment at 24 months and significant differences between
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age groups. (F) For each network pair constituting a primary result, functional connections are colored according to both 1) the sign of the relationship between fc value and behavior (light blue to dark blue to magenta colors reflect negative brain-behavior relationships, and green to yellow to red colors reflect positive brain-behavior relationships) and 2) the proportion of individual fcMRI values above zero (light blue and green denote that – across subjects – the ROI pair contains only negative fcMRI values while magenta and red reflect ROI pairs with only positive fcMRI values). The proportion of fcMRI data that are positive for each brain-behavior relationship is also displayed in a scatter plot.
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Supplemental Figure S3. Stereotyped Enrichment Details. Related to Figure 3. (A-D) Format similar to Supplemental Figure S2. (E) Three network pairs showed both significant enrichment at a given age and significant differences across age groups: Vis-tDMN at 12 mo and tDMN-pFPC and SubCtx-DAN at 24 mo. (F) Format similar to Supplemental Figure S2.
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Supplemental Figure S4. Ritualistic/Sameness Enrichment Details. Related to Figure 4. (AD) Format similar to Supplemental Figure S2. (E) The Vis-tDMN network pairs showed both significant enrichment at 12 mo and significant differences across age groups while the Vis-pFPC network pair showed significant enrichment at both 12 mo and 24 mo. (F) Format similar to Supplemental Figure S2.
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Supplemental Figure S5. No network level results for strong fc-self-injurious associations. (A) Analyses follow those outlined in Figure 2 and Supplemental Figure S2. (A-B) Matrices describe the relationships between fc and self-injurious behavior scores. (C) The Vis-DAN network pair showed significant clustering of strong (p ≤ 0.05) negative brain-behavior relationships at 24 mo but not at 12 mo, as assessed with complementary chi-square and hypergeometric statistics. (For brevity, only the hypergeometric values are shown here.) (D) A McNemar test evaluated differences in brain-behavior associations between age groups. (E) No network pairs showed both significant enrichment at a given age and significant differences across age groups.
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Supplemental Figure S6. Brain-behavior analyses using adult functional networks. ROIs were grouped based on an adult functional network structure. These analyses implicated a highly similar subset of functional network pairs but resulted in fewer primary results.
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Supplementary Table S1. RBS-R factors (17) RBS-R Definition items Restricted 40-43 Limited range of focus, interest or activity Apparently purposeless movements or actions that are repeated in a similar Stereotyped 1-6 manner Ritualistic/ Performing activities of daily living in a similar manner, 23-39 Sameness or resistance to change and insistence that things stay the same Movement or actions that have the potential to cause redness, bruising, or Self-Injurious 7-14 other injury to the body, and that are repeated in a similar manner
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Supplementary Table S2. Effect of site and age in months on RBS-R score
Stereotyped Self-Injurious
0.29 (0.79) 0.44 (0.86) 0.64 (1.2) 0.72 (1.3) 0.40 (0.80) 0.54 (1.1) 0.61 (1.6) 1.4 (1.9)
Effect of site
F 1.3 8.8x10-1 8.8x10-1 3.7x10-2 2.5 6.2x10-1 2.4 6.9x10-1
p 2.7x10-1 4.5x10-1 4.5x10-1 9.9x10-1 5.8x10-2 6.0x10-1 6.4x10-2 5.6x10-1
Correlation with age in months rho 8.3x10-3 8.7x10-2 6.0x10-2 -3.1x10-2 7.9x10-2 -2.4x10-2 7.7x10-2 -3.9x10-2
p 8.5x10-1 8.4x10-2 1.9x10-1 5.5x10-1 8.4x10-2 6.3x10-1 9.1x10-2 4.4x10-1
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RBS-R score (SD)
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Table S3. Significant network pair statistics HG asymp. p
HG FPR p
MN χ2
MN asymp. p
MN FDR p
1.5x10-25
1.2x10-3
1.3x10-17
9.7x10-4
14.1
8.8x10-5
4.7x10-2
4.0x10-15
4.6x10-3
5.6x10-11
4.3x10-3
2.57
5.4x10-2
3.3x10-1
1.7x10-39
4.9x10-4
1.5x10-30
2.1x10-4
68.6
5.9x10-17
2.3x10-4
2.7x10-19
3.3x10-3
2.5x10-14
2.8x10-3
32.1
7.2x10-9
5.3x10-3
1.9x10-13
6.4x10-3
3.3x10-9
7.6x10-3
0.86
1.8x10-1
5.2x10-1
<1.0x10-33
1.1x10-6
8.6x10-39
4.3x10-5
50.4
6.1x10-13
8.1x10-4
3.1x10-27
1.1x10-3
1.8x10-16
1.4x10-3
12.2
2.3x10-4
5.5x10-2
8.7x10-18
3.3x10-3
5.0x10-12
3.6x10-3
5.12
1.2x10-2
1.8x10-1
6.1x10-20
2.9x10-3
1.2x10-13
3.1x10-3
24.6
3.5x10-7
1.1x10-2
2.3x10-18
3.5x10-3
5.7x10-14
2.9x10-3
28.7
4.3x10-8
6.7x10-3
3.7x10-36
6.6x10-4
1.9x10-23
9.2x10-4
20.8
2.6x10-6
5.6x10-2
<1.0x10-33
1.1x10-6
<1.0x10-33
1.1x10-6
165
5.1x10-38
1.9x10-5
1.3x10-11
1.1x10-2
5.5x10-10
9.8x10-3
23.8
5.2x10-7
4.3x10-2
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χ2 FPR p
158
12
757
12
45.8
12
95.8
1.3x10-22
2.3x10-3
5.6x10-18
2.0x10-3
28.2
5.3x10-8
3.0x10-2
12
71.7
2.5x10-17
4.4x10-3
1.9x10-13
4.5x10-3
3.4
3.3x10-2
3.9x10-1
44.5
2.5x10-11
1.1x10-2
3.9x10-9
1.1x10-2
3.4
3.3x10-2
3.9x10-1
tDMN24 145 pFPC Self-injurious behavior
2.6x10-33
8.8x10-4
1.8x10-22
8.3x10-4
20.3
3.3x10-6
5.9x10-2
Vis-DAN
6.6x10-22
2.7x10-3
5.8x10-18
1.9x10-3
24.3
4.0x10-7
2.4x10-2
Vis-pFPC Vis-pFPC
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χ2 asymp. p
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Age χ2 group statistic (mo) Restricted behavior Vis12 109 tDMN Vis12 61.7 SMN2 tDMN24 173 DAN tDMN24 80.6 pFPC Stereotyped behavior Vis-Vis 12 54.1 Vis12 321 tDMN Vis-pFPC 12 117 pFPC12 73.8 pcDMN pFPC24 83.6 tDMN SubCtx24 76.4 DAN Ritualistic/sameness behavior Network pair
24
24
92.6
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Supplement
Supplemental References
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1. Pruett JR, Jr., Kandala S, Hoertel S, Snyder AZ, Elison JT, Nishino T, et al. (2015): Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data. Developmental Cognitive Neuroscience 12:123-133. 2. Wolff JJ, Gerig G, Lewis JD, Soda T, Styner MA, Vachet C, et al. (2015): Altered corpus callosum morphology associated with autism over the first 2 years of life. Brain. 138:20462058. 3. Eggebrecht AT, Elison JT, Feczko E, Todorov A, Wolff JJ, Kandala S, et al. (2017): Joint Attention and brain functional connectivity in infants and toddlers. Cereb Cortex. 27:17091720. 4. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL (2011): Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 54:313-327. 5. Smyser CD, Inder TE, Shimony JS, Hill JE, Degnan AJ, Snyder AZ, et al. (2010): Longitudinal analysis of neural network development in preterm infants. Cereb Cortex. 6. Ojemann JG, Akbudak E, Snyder AZ, McKinstry RC, Raichle ME, Conturo TE (1997): Anatomic localization and quantitative analysis of gradient refocused echo-planar fMRI susceptibility artifacts. Neuroimage. 6:156-167. 7. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE (2014): Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage. 84:320341. 8. Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R (1996): Movement-related effects in fMRI time-series. Magn Reson Med. 35:346-355. 9. Philip RC, Dauvermann MR, Whalley HC, Baynham K, Lawrie SM, Stanfield AC (2012): A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neurosci Biobehav Rev. 36:901-942. 10. Cohen AL, Fair DA, Dosenbach NU, Miezin FM, Dierker D, Van Essen DC, et al. (2008): Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage. 41:45-57. 11. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. (2011): Functional network organization of the human brain. Neuron. 72:665-678. 12. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012): Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 59:2142-2154. 13. Rosvall M, Bergstrom CT (2008): Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci U S A. 105:1118-1123. 14. Backes C, Ruhle F, Stoll M, Haas J, Frese K, Franke A, et al. (2014): Systematic permutation testing in GWAS pathway analyses: identification of genetic networks in dilated cardiomyopathy and ulcerative colitis. BMC Genomics. 15:622. 15. Rivals I, Personnaz L, Taing L, Potier MC (2007): Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics. 23:401-407. 16. Khatri P, Sirota M, Butte AJ (2012): Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 8:e1002375. 17. Bodfish JW, Symons FJ, Parker DE, Lewis MH (2000): Varieties of repetitive behavior in autism: comparisons to mental retardation. J Autism Dev Disord. 30:237-243.
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S2451-9022(18)30247-7
DOI:
10.1016/j.bpsc.2018.09.008
Reference:
BPSC 339
To appear in:
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Received Date: 2 August 2018 Accepted Date: 1 September 2018
Please cite this article as: McKinnon C.J., Eggebrecht A.T., Todorov A., Wolff J.J., Elison J.T., Adams C.M., Snyder A.Z., Estes A.M., Zwaigenbaum L., Botteron K.N., McKinstry R.C., Marrus N., Evans A., Hazlett H.C., Dager S.R., Paterson S.J., Pandey J., Schultz R.T., Styner M.A., Gerig G., Schlaggar B.L., Petersen S.E., Piven J., Pruett J.R., Jr & for The IBIS Network, Restricted and repetitive behavior and brain functional connectivity in infants at risk for developing autism spectrum disorder, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2018), doi: https://doi.org/10.1016/ j.bpsc.2018.09.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Title: Restricted and repetitive behavior and brain functional connectivity in infants at risk for developing autism spectrum disorder
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Authors: Claire J. McKinnon1,2,†*, Adam T. Eggebrecht3,†, Alexandre Todorov1, Jason J. Wolff4, Jed T. Elison5, Chloe M. Adams1, Abraham Z. Snyder3, Annette M. Estes6, Lonnie Zwaigenbaum7, Kelly N. Botteron1,3, Robert C. McKinstry3, Natasha Marrus1, Alan Evans8, Heather C. Hazlett9, Stephen R. Dager10, Sarah J. Paterson11, Juhi Pandey12, Robert T. Schultz12, Martin A. Styner9, Guido Gerig13, Bradley L. Schlaggar1,3,14, Steven E. Petersen3,14,15,16, and Joseph Piven9,‡ & John R. Pruett, Jr.1,‡ , for The IBIS Network∆ Affiliations: 1
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Department of Psychiatry; Washington University School of Medicine; 660 S. Euclid Ave, St Louis, MO, 63110; USA.
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Biological Sciences Division; University of Chicago; 5841 S. Maryland Ave., Chicago, IL, 60637; USA.
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Mallinckrodt Institute of Radiology; Washington University School of Medicine; 660 S. Euclid Ave, St Louis, MO, 63110; USA.
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Department of Educational Psychology; University of Minnesota; 56 East River Road, Minneapolis, MN, 55455; USA. 5
Institute of Child Development; University of Minnesota; 51 East River Parkway, Minneapolis, MN, 55455; USA.
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Department of Speech and Hearing Sciences; University of Washington, Seattle; UW Autism Center Box 357920, Seattle, WA, 98195; USA.
Department of Pediatrics; University of Alberta; c/o Autism Research Centre, Glenrose Rehabilitation Hospital E209, 10230, 111th Avenue, Edmonton, AB T5G0B7; Canada.
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McConnell Brain Imaging Center; Montreal Neurological Institute; McGill University; 3801 University St., Montreal, QC, H3A 2B4; Canada.
The Carolina Institute for Developmental Disabilities; University of North Carolina at Chapel Hill; 101 Renee Lynn Court, Carborro, NC, 277599-3367; USA. 10
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Department of Radiology and Bioengineering; University of Washington, Seattle; 1701 NE Columbia Rd, Seattle, WA, 98195-7920; USA. Department of Psychology; Temple University; 1701 N. 13th St., Philadelphia, PA, 19122; USA.
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Center for Autism Research, Children’s Hospital of Philadelphia; Department of Psychiatry, University of Pennsylvania; Civic Center Blvd, Philadelphia, PA, 19104; USA.
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Tandon School of Engineering; New York University; 2 Metro Tech Center, Brooklyn, NY, 11201; USA. 14
Department of Neurology; Washington University School of Medicine; 660 S. Euclid Ave, St Louis, MO, 63110; USA.
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Department of Psychological and Brain Sciences; Washington University in St. Louis; 1 Brookings Dr., St Louis, MO, 63130; USA. 16
†
Shared first authors
‡
Shared senior authors
*Correspondence to: Claire McKinnon Biological Sciences Division University of Chicago 5841 S. Maryland Ave. Chicago, IL 60637, USA Phone: 773-795-3849 Email: [email protected]
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See Acknowledgements for information about The Infant Brain Imaging Study (IBIS) Network
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Department of Biomedical Engineering; Washington University in St. Louis; 1 Brookings Dr., St Louis, MO, 63130; USA.
Short title: RRBs and brain fc in infants at risk for developing ASD
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Keywords: Autism spectrum disorder; brain development; restricted and repetitive behavior; functional connectivity; functional magnetic resonance imaging; infant Number of words in abstract: 250
Number of words in main text: 3971
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Number of figures: 6 Number of tables: 1
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Number of supplemental information: 1
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Abstract
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Background—Restricted and repetitive behaviors (RRBs), detectable by 12 months (mo) in
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many infants later diagnosed with autism spectrum disorder (ASD), may represent some of the
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earliest behavioral markers of ASD. However, brain function underlying the emergence of these
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key behaviors remains unknown.
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Methods—Behavioral and resting-state functional connectivity (fc) magnetic resonance imaging
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data were collected from 167 children at high and low familial risk for ASD at 12 and 24
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mo (n=38 at both time points). Fifteen infants met criteria for ASD at 24 mo. We divided RRBs
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into four subcategories (restricted, stereotyped, ritualistic/sameness, self-injurious) and used a
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data-driven approach to identify functional brain networks associated with the development of
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each RRB subcategory.
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Results—Higher scores for ritualistic/sameness behavior were associated with less positive fc
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between visual and control networks at 12 and 24 mo. Ritualistic/sameness and stereotyped
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behaviors were associated with less positive fc between visual and default mode networks at 12
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mo. At 24 mo, stereotyped and restricted behaviors were associated with more positive fc
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between default mode and control networks. Additionally, at 24 mo, stereotyped behavior was
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associated with more positive fc between dorsal attention and subcortical networks, while
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restricted behavior was associated with more positive fc between default mode and dorsal
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attention networks. No significant network-level associations were observed for self-injurious
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behavior.
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Conclusion—These observations mark the earliest known description of functional brain
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systems underlying RRBs, reinforce the construct validity of RRB subcategories in infants, and
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implicate specific neural substrates for future interventions targeting RRBs.
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Introduction
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In the first two years of typical childhood development, restricted and repetitive behaviors
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(RRBs) contribute to a cascade of progressive events that engender flexible and complex patterns
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of goal-directed behavior. However, recent evidence suggests that elevated levels of RRBs
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comprise some of the earliest emerging behavioral manifestations of autism spectrum disorder
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(ASD) and are observed as early as 12 months of age in infants later diagnosed with ASD (1-4).
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Along with deficits in social communication and interaction, RRBs comprise the defining core
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features of ASD (5), a common neurodevelopmental disorder that is among the most highly
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heritable of psychiatric conditions (6). The elevated levels of RRBs characteristic of ASD are
33
impairing; the intensity and inflexibility associated with these repetitive actions and limited
34
interests constrains opportunities to access environmental input important for learning and social
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development (7). Additionally, RRBs in both children and adults have been linked to alterations
36
likely to broadly affect task performance, including abnormal sensory processing and deficits in
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cognitive control and executive functioning (8-14). Neural correlates of ASD include atypical
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local and network-level brain structure and resting-state functional networks from early
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development through adulthood (14-30). Elucidating the relationships between RRBs and the
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maturation of the brain’s functional networks during typical and atypical early development may
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enhance early risk assessment, inform developmental models of ASD pathogenesis, and provide
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a neurophysiological foundation for novel interventions focused on RRBs.
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The RRB domain encompasses a heterogeneous set of behaviors—intense
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preoccupations, stereotyped movements, and resistance to change (5)—that may be better
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conceptualized as distinct subcategories (7, 31, 32). Factor analytic studies in both young
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children and adults support the existence of considerable structure within the RRB domain,
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where a five-factor model both provides superior precision in RRB behavioral profiling (33, 34)
48
and highlights developmental trajectories of unique RRB subcategories evident as early as 12
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months (4, 31). However, characterizing the specific neural contributions to the emergence of
50
these behaviors has proven challenging (35-38), due in part to the significant methodological
51
challenges of imaging brain function in infants and toddlers. Most studies using either task-based
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functional MRI (fMRI) or resting-state functional connectivity MRI (fcMRI) have focused
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largely on adulthood, rather than the first years of life when such behaviors begin to emerge.
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Here, we aimed to characterize the relationship between subcategories of RRBs and functional connectivity (fc) within and between putative brain networks over a pivotal time in
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development for both ASD-specific behavioral features (3, 4) and functional network
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organization (39-42). To quantify RRBs across four subcategories in a sample of 12-month old
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infants and 24-month old toddlers at high and low familial risk for ASD, we used the Repetitive
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Behavior Scale-Revised (RBS-R) (32). In the same infants, brain functional network architecture
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was measured using resting-state fcMRI. We took a data-driven approach to assessing brain-
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behavior relationships because, as both RRBs and brain function exhibit significant maturation
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throughout early childhood, findings from older individuals may not extend to infants and
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toddlers. Functional connections most associated with RBS-R subcategory factor scores were
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identified using generalized linear modeling. We then used enrichment analyses, adapted from
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large-scale genome-wide association studies, to identify network pairs exhibiting a significantly
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increased density of these strong brain-behavior relationships (43, 44). This established
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statistical method facilitated a brain-wide approach while constraining the burden of multiple
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comparisons. We hypothesized that: (1) the functional connections most strongly associated with
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RRBs would involve the default mode network, the frontoparietal control network, the salience
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network, and striatal regions of the subcortical network (38, 45) – networks previously
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implicated with RRBs in studies on older participants, and (2) RBS-R subcategories previously
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combined into an alternate behavioral category (33, 34) would be associated with overlapping
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patterns of functional network pairs.
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74 Materials and Methods
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Participants
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Participants were recruited as part of an ongoing, multisite, Autism Centers of Excellence
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Network study — the Infant Brain Imaging Study (IBIS), which has collected a large infant
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sibling sample with prospective brain-behavioral data across a crucial time window during which
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ASD develops. Individuals were assessed and scanned at each of four clinical sites: University of
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North Carolina, University of Washington, The Children’s Hospital of Philadelphia, and
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Washington University in St. Louis. The research protocol was approved by the institutional
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review boards at all clinical sites, and parents provided written informed consent after receiving
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a detailed description of the study.
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Infants were classified as high-risk (HR) if they had at least one sibling with a diagnosis
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of ASD, or low-risk (LR) if they had at least one typically developing older sibling and no first
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or second degree relatives with ASD or an intellectual disability. Participants were subsequently
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assigned diagnostic outcome labels: HR-ASD-positive, HR-ASD-negative, or LR-ASD-negative,
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depending on clinical best estimate, applying the DSM-IV-TR (5) checklist to all available data
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at 24 months. LR-ASD-positive subjects were omitted from the analyses (Table 1). Because HR
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children show elevated occurrence rates of RRBs relative to LR controls (4), this sample
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provided a sufficient range of RBS-R scores to adequately detect relationships between
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behavioral scores and dimensional metrics of brain function. All included participants
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contributed both behavioral and fcMRI data at visits corresponding to 12 and/or 24 months of
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age (range: 11.1-15.0 months and 22.4-27.0 months, respectively). All datasets were subject to
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stringent fcMRI quality control criteria and IBIS behavioral and structural MRI inclusion criteria
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(21) (see the Supplement for details). Age groups did not significantly differ by proportion of
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children later diagnosed with ASD (Fisher’s exact test, p=0.36), HR participants (p=1.0), girls
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(p=0.47), or cognitive development (Mullen composite standard score, Welch’s t-test, p=0.24).
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100 Behavioral assessment
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RRBs were assessed using the RBS-R (32), a parent/caregiver rated questionnaire consisting of
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43 items that has been validated for use among toddlers and preschool age children (4, 33, 34).
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Responses focused on the counts of items-endorsed rather than on severity scores, as the latter
105
may be more susceptible to rater bias (4). The 43 items have been conceptually grouped into six
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subscales: stereotyped behavior, self-injurious behavior, compulsive behavior, ritualistic
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behavior, sameness behavior, and restricted behavior (32). In line with previous factor analytic
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studies (33, 34), ritualistic and sameness subtests were combined into a single factor. The
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compulsive factor was excluded as this subscale was not developmentally appropriate for the
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subjects included in the current study (4). The present study focused on the remaining four
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factors: restricted, stereotyped, ritualistic/sameness, and self-injurious behaviors (Figure 1A-D;
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Supplemental Table S1). There was no effect of site on RRB scores, and age-in-months was not
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correlated with RRB score (Supplement; Supplemental Table S2).
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Imaging acquisition
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Anatomical and functional brain imaging was carried out at all clinical sites using identical,
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cross-site calibrated 3-T Siemens TIM trio scanners (Siemens Medical Solutions, Malvern, PA),
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each equipped with standard 12-channel head coils. All infants were scanned during natural sleep
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(43). Each of two to three acquisitions was comprised of 130 temporally contiguous frames
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spanning 5.4 minutes. See the Supplement for details.
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fMRI preprocessing and fidelity optimization
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Data were preprocessed to reduce artifacts (i.e. BOLD signal changes not resulting from neural
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activity) and spatially registered to a 3 mm isotropic space using previously outlined procedures
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(41, 46). Small degrees of head motion-induced artifact can significantly alter correlations in
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resting-state data and confound interpretations of functional connectivity, particularly in studies
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of development where age is a factor of interest (47-49). To optimize data fidelity and minimize
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artifact, stringent thresholds in motion censoring “scrubbing” based on frame-to-frame
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displacement (FD; calculated as the sum of the absolute values of the six different realignment
130
estimates—X, Y, Z, pitch, yaw, roll—at every time point (50)), number of contiguous frames,
131
and total frame number were maintained. Frames with calculated FD ≥ 0.2 mm were marked for
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censoring. See the Supplement for further details. Exactly 150 frames, corresponding to 6.25
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minutes, of high-quality, low-motion MRI data were used from each participant within each age
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group.
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fcMRI preprocessing
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Following previously described procedures (50), data were voxel-wise demeaned and detrended
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within runs, while censored frames were ignored. Nuisance waveforms (including the global
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signal) were regressed voxelwise from the data, ignoring censored frames (Supplement). In
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frames marked for censoring, data were replaced by interpolated values computed by least-
141
squares spectral analysis (50, 51). Interpolated data were only included for bandpass filtering and
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did not factor into correlation values. Finally, the data were spatially smoothed using a Gaussian
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kernel (6 mm FWHM isotropic).
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144 Definition of ROIs and functional connectivity computation
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The derivation of 230 regions of interest for studies on this population was previously described
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(41, 43) (Supplemental Figure S1). ROI-specific timecourses were calculated by averaging the
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timecourses of all voxels contained within 10 mm diameter spheres. Functional connectivity (fc)
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values were calculated as the pairwise zero-lag Pearson correlation between each of the 26,335
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pairs of ROI timecourses, and then Fisher-z transformed.
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Derivation of putative functional networks in infants and toddlers
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We utilized a previously described cross-age functional brain network model composed of these
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230 ROIs from a longitudinal cohort of N=48 children with clean fcMRI data at both 12-and 24-
155
months (43) (see the Supplement for details). This model of the combined infant-toddler
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functional brain networks included 13 putative networks with naming informed by published
157
adult networks (47): visual (Vis), temporal default mode network (tDMN), posterior cingulate
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DMN (pcDMN), anterior DMN (aDMN), somato-motor network (SMN), somato-motor network
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2 (SMN2), dorsal attention network (DAN), posterior frontal parietal control network (pFPC),
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anterior FPC (aFPC), subcortex (SubCtx), cingulo-opercular (CO), posterior CO (pCO), and
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salience (Sal) (Figure 1; Supplemental Figure S1) (43, 52). To generate complementary
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analyses using adult functional networks, we adapted a functional brain network structure
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derived from a published independent fcMRI dataset of typical adults (53).
164 Statistical Analysis
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Differences in RRB subcategory scores across age were calculated across the larger IBIS sample
167
of participants who contributed behavioral data (N = 467 at 12 months, N = 379 at 24 months,
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where n = 327 had data at both time points) with the Wilcoxon signed-rank test (alpha level of
169
0.0125 reflecting Bonferroni correction for the four behaviors tested). Using the matched fcMRI-
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RBS-R data, generalized linear modeling was used to examine the predictive relationship
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between brain fc and RRB subcategory scores separately for each of the 26,335 ROI pairs, at
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each time point, yielding two 26,335-element matrices of regression coefficients for each RRB
173
subcategory (e.g. Figure 2A). Specifically, we fit a fixed-effect negative binomial regression
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with the relevant RBS-R subcategory factor as outcome and the ROI-ROI fc values as predictors.
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The negative binomial distribution provided optimal modeling for the RBS-R inventories that
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contained heavily zero-weighted integer count data (Figure 1 A-D; (14)), based on goodness-of-
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fit statistics that indicated superiority to a Poisson distribution model. Enrichment analyses were
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used to determine whether strong (defined as p ≤ 0.05, uncorrected; e.g., Figure 2B) brain-
179
behavior relationship values (i.e., regression coefficients between RBS and fc for each ROI-pair)
180
were significantly clustered within specific network pairs (43) (see the Supplement for details).
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A McNemar test was performed to determine whether patterns of brain-behavior relationships
182
significantly differed between the 12- and 24- month groups. Stringent, brain-wide empirical
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significance levels—reflecting the 1.25% false positive rate—were determined using
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randomization (43). An alpha of 0.0125 was used to account for the four behavioral factors
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analyzed. The randomization procedure preserved the correlation and missing-data patterns in
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the data (e.g., infants assessed at 12 months only) and produced two separate brain-wide null
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distributions for statistical evaluation of the data—one for each time point (Supplement).
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Primary results include network pairs either significantly enriched at both time points, or
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significantly enriched at one age and significantly different in their set of brain-behavior
190
relationships between age groups (e.g. Figure 2D). In contrast, network pairs that exhibit
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significant enrichment at one time point –but do not differ statistically from null results at the
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other time –are presented as discovery findings that may provide potential targets for future
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hypothesis-driven studies.
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Results
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Behavioral characterization
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We analyzed behavioral and neuroimaging data collected from infants at 12 (n=118) and/or 24
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months of age (n=87; n=38 children provided data at both ages) (Table 1). Restricted interests,
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stereotyped mannerisms, and self-injurious behavior scores did not show significant differences
200
across age (restricted p=0.77; stereotyped p=0.72; self-injurious=0.61), whereas
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ritualistic/sameness behavior showed a significant age-related increase (p=1.8x10-4; consistent
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with previous findings (4)).
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Restricted behavior associations with functional connectivity
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The primary findings in relation to restricted behavior include only two of the possible 91 total
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network pairs: the tDMN-DAN and tDMN-pFPC at 24 mo (Figure 2). Each network pair
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exhibited significant clustering of positive brain-behavior relationships (Figure 2D), meaning 11
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that more positive fc values between these brain regions were associated with higher restricted
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behavior scores. Though the associations between fcMRI and restricted scores were positive
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within these two network pairs, the range of the fcMRI values, themselves, within the tDMN-
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DAN and tDMN-pFPC were remarkably distinct (Supplemental Figure S2). Specifically, in
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77% of the tDMN-DAN ROI pairs, predominantly negative fc values were exhibited. The
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observation of a positive relationship between fc and restricted behavior in the context of
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predominantly negative fc for ROI pairs indicates that strongly negative tDMN-DAN fc is
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associated with fewer restricted behaviors. In contrast, 70% of tDMN-pFPC connections
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contributing to enrichment showed predominantly positive fc values. In addition to these primary
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findings, two discovery-level findings were also observed at 12 mo: negative fc-restricted
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relationships in Vis-tDMN and positive fc-restricted relationships in Vis-SMN2.
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Stereotyped behavior associations with functional connectivity
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Primary findings include three network pairs: Vis-tDMN at 12 months, as well as DAN-SubCtx
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and tDMN-pFPC at 24 months (Figure 3). At 12 months, Vis-tDMN connections showed
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significant clustering of strong negative brain-behavior relationships (Figure 3D), with 77%
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exhibiting predominantly negative fc (Supplemental Figure S3). In contrast, at 24 months,
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greater stereotyped scores were associated with more positive fc between tDMN-pFPC and
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DAN-SubCtx (Figure 3). Of tDMN-pFPC connections contributing enrichment, 83% showed
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primarily positive fc, while 89% of implicated DAN-SubCtx ROI pairs showed primarily
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negative fc values (Supplemental Figure S3). Of the subcortical ROIs contributing to
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enrichment, four ROIs, all located in the putamen/lentiform nucleus, were implicated markedly
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more frequently than the others (MNI coordinates: -31.4, -11.5, -0.3; 30.5, -13.9, 1.7; 23.3, 10.2,
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1.5; 28.5, 0.8, 4.0). Discovery findings include three network pairs at 12 months (Figure 3C),
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including enrichment of positive fc-stereotyped relationships within the visual network (Vis-Vis)
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and negative fc-stereotyped relationships within the Vis-pFPC and pcDMN-pFPC (Figure 3B).
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Ritualistic/sameness behavior associations with functional connectivity
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Primary findings include higher ritualistic/sameness scores associated with less positive fc
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between Vis-tDMN at 12 months (with 82% of connections showing primarily negative fc)
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(Figure 4; Supplemental Figure S4), as well as less positive fc between Vis-pFPC at both 12
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and 24 months (with primarily negative fc in 85% of connections at 12 months and 90% of
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connections at 24 months). Of the implicated Vis-pFPC connections, 16% (9/55) of connections
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at 12 months and 23% (9/39) of connections at 24 months were implicated at both time points.
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Discovery findings included four additional network pairs: enrichment of positive fc-ritualistic
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relationships within Vis-Vis and Vis-pcDMN at 12 months, enrichment of negative fc-ritualistic
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relationships within tDMN-pcDMN at 12 months, and enrichment of positive fc-ritualistic
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relationships between the tDMN-pFPC at 24 months (Figure 4).
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Self-injurious behavior associations with functional connectivity
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No primary level findings were observed in the fc-self-injurious relationships at either age
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(Supplemental Figure S5). A discovery level finding was observed at 24 months within Vis-
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DAN.
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Convergence across RRB subcategories
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As detailed above, Vis-tDMN was associated with both stereotyped and ritualistic/sameness
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behaviors at 12 months, and at 24 months tDMN-pFPC was associated with both stereotyped and
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restricted behaviors (Figure 5 for summary of results). In each case, convergence at the network
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level was also represented at the level of individual ROI connections (Figure 6). Present in the
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convergent findings at both ages are tDMN ROIs predominantly distributed around the lateral
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right temporal lobe and the right temporal-parietal junction.
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Discussion
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Though exhibitions of RRBs reflect crucial stages of early typical development, accumulating
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evidence suggests that early elevation of these behaviors may provide a potent early risk marker
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for later diagnosis of ASD. Our primary findings (summarized in Figure 5) reveal that: (1)
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neural signatures of RRB subcategories are distinct between 12 and 24 months of age, with the
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single exception of negative Vis-pFPC fc associations with ritualistic/sameness behavior (Figure
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4); (2) at 12 months of age, fc between Vis-tDMN is significantly associated with both
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ritualistic/sameness and stereotyped behaviors (Figures 3,4,6); (3) at 24 months of age, tDMN-
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pFPC fc is significantly associated with both stereotyped and restricted behaviors (Figures
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2,3,6); (4) at 24 months of age, fc between tDMN-DAN is significantly associated with restricted
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behavior (Figure 2); and (5) at 24 months of age, fc between DAN-SubCtx is significantly
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associated with stereotyped behavior (Figure 3).
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First, our results show that higher scores for restricted behavior—defined by a limited
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range of focus, interest, or activity—are associated with reduced anti-phase correlations (less
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negative fc) between the tDMN and the DAN, as well as more positive fc between tDMN-pFPC.
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Interactions between regions within the DAN, the default mode network (DMN), and the fronto-
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parietal control (FPC) network, have been shown to shift connectivity and network affiliation in
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a dynamic, flexible, and adaptive manner to support specific task demands (45, 54-61). Increased
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exhibition of RRBs has been shown to be associated with task-evoked and resting-state fc
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patterns of networks including the DMN and central executive networks (corresponding to our
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FPC (45)). An anti-correlated relationship between the DAN and DMN is well documented in
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both task and resting-state fMRI studies in adults (62-64), with weaker anti-correlations
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predicting greater impairment in attention and inhibitory control (65), and providing a possible
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mechanism for limited cognitive flexibility. Further, resting-state anti-correlated patterns
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between DAN and DMN have been observed to emerge over the first year of life and become
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increasingly anti-correlated by 24 months (66). Our results add empirical support to the
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hypothesis that inverse functional relationships between DAN and DMN regions reflect healthy
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typical development, and that abnormal network dynamics between the tDMN, DAN, and pFPC
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may underlie rigid and repetitive behaviors and interests (45, 55, 66-69).
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The stereotyped behavior factor, which includes apparently purposeless movements repeated in a similar manner, was associated with more positive fc between tDMN-pFPC and
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DAN-SubCtx at 24 months, as well as decreasing fc between Vis-tDMN at 12 months. The
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convergent findings between restricted and stereotyped behaviors (tDMN-pFPC) are consistent
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with factor analytic studies that grouped items from these two behaviors into a single factor (33,
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34). Additionally, we observed that DAN connectivity with subcortical regions, primarily the
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putamen/lentiform nucleus and cerebellum, was uniquely associated with stereotyped behavior.
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These findings provide further empirical support for significant involvement of cortico-striatal-
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thalamo-cortical circuitry in stereotypies (70-72). Further, atypical recruitment and regulation of
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visual cortical regions and atypical visual attention have been linked with RRB symptomology
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(73-75) and abnormalities in the control of visual attention have been shown to persist
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throughout infancy in children who are later diagnosed with ASD (23, 76-78).
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The ritualistic/sameness behavior factor, which contains items related to visual preference (e.g., likes same movie/scene played continuously), was associated with less positive
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fc between Vis-tDMN (79, 80) at 12 months, as well as between Vis-pFPC at both 12 and 24
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months. Studies in older children have shown similar associations between greater ASD
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symptomology and reduced resting-state fc between the visual network and other large-scale
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functional networks including motor and salience networks (74, 81). Given previous studies that
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have linked RRBs to atypical sensory response patterns including hyper responsivity to sensory
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stimuli and sensory seeking behavior (9-11, 14), our findings support the hypothesis that early
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disordered fc involving the visual network may engender later disruptions in higher order
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behaviors (82, 83).
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The convergence at 12 months between stereotyped and ritualistic/sameness behaviors
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(Vis-tDMN) was unexpected, as different RBS-R factor schemes categorize these two behaviors
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as lower vs higher order, respectively (34, 84, 85). Although these behaviors show distinct
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developmental trajectories and outward manifestations, these are the two RBS-R factors that
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show the most robust associations with atypical sensory features (10, 11). The highly
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overlapping involvement of Vis-tDMN suggests that the two behavioral subcategories may share
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a common etiology.
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Our convergent findings across RRB subcategories highlight connectivity between
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temporal regions of the infant DMN with Vis and pFPC networks. Of the 22 tDMN ROIs, 17
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were located along either side of the superior temporal sulcus (STS; as were a number of
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implicated pFPC ROIs). Both Vis-tDMN and tDMN-pFPC included a subgroup of tDMN ROIs
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located in the right posterior STS (pSTS) and nearby temporal parietal junction region. The right
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pSTS has been implicated in social functions affected in ASD, including processing biological
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motion and eye gaze (86), as well as basic perceptual functions (e.g., audiovisual integration)
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(87-91) and the control of visual attention (92). Decreased activation of the STS during
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visuomotor learning in individuals with ASD has been linked to more severe RRBs (93). Thus,
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the observed RRB-fc relationships involving the STS, particularly right STS, by 12 months of
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age, support common neurophysiological bases for the development of atypical social and RRB
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behaviors and the presentation of ASD as a clinical syndrome. This overlap in neural substrates
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is also consistent with prior behavioral studies showing that both social and RRB features load
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onto a primary factor underlying the continuum of autistic traits (94).
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Several limitations and future directions merit discussion. First, because we included participants with longitudinal and cross-sectional data in order to maximize sample size at each
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time point, cross-age comparisons involve some non-overlapping participants. Future studies
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utilizing exclusively longitudinal data to elucidate within-subject trajectories will be required to
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better understand how these networks instantiate behavior across development. Second,
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differences may exist between high- and low-risk groups both within and across age groups in
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brain-behavior relationships, and the network architecture underlying RRB subcategories may
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differ in children who develop ASD. However, our study was not designed or powered to
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address these questions; rather, this sample provided the range of behavior required to adequately
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elucidate the observed brain-RRB relationships. These behaviors are continuously distributed,
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and by capitalizing on the variance afforded by this mixed group we maximized our chances of
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identifying important relationships for this initial study of fcMRI correlates of RRBs in the first
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two years of life. This work sets the stage for future studies comparing brain-behavior
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relationships in larger age-specific, risk, and outcome sub-groups. Third, though our set of
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subcortical and cerebellar ROIs was not comprehensive, the functionally-defined ROIs in the
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current study are well vetted (41), and recent related work demonstrated the utility and validity
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of enrichment analyses using these ROIs as a novel method for analyzing brain-behavior
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relationships (43, 52). Fourth, our fcMRI processing included global signal regression (GSR), an
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established approach to removing motion-induced artifacts from fcMRI data (50, 95). Recent
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studies have established that GSR in combination with volume censoring is superior to other
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denoising procedures for the removal of motion artifact (96), though a limitation of this approach
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is that current methods cannot rule out the concomitant removal of genuine neural signal. Given
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that motion-related artifacts present a major confound in fc analyses (47, 49, 95, 97), our
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approach conservatively accounted for motion-related artifacts to minimize the risk of spurious
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interpretations of fc-behavior relationships. Finally, differences between the infant/toddler and
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adult networks may be attributed to age-related development, but may also reflect
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methodological differences due to possible changes in functional network organization during
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sleep (98-100).
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The implicated network pairs for RRB subcategories were different from those
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implicated for initiation of joint attention (43) and also for walking and gross motor behaviors
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(52). These non-overlapping findings support the specificity of the brain-behavior relationships
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revealed with our fcMRI enrichment approach. Future studies that systematically analyze the
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overlap between networks enriched for specific RRB subcategories and other early emerging
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behavioral features associated with ASD, particularly atypical sensory response patterns and
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motor development, could provide further insight into the complex relationship between
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dimensional aspects of behavior and underlying neurobiology in typical and atypical
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development. Comparing the trajectories of these early developmental brain-behavior
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relationships across different outcome groups would offer the potential for future identification
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of predictive biomarkers to aid in distinguishing typical and atypical early development.
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Furthermore, given the paucity of intervention practices targeted toward RRBs in ASD (101),
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changes in associated brain fc may be useful in tracking the efficacy of novel interventions.
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Acknowledgments and Disclosures We wish to thank the families and children for their time and participation. We thank Sarah Hoertel for key early analyses and for her help with guiding the brain-behavior analysis strategy.
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We also thank Leigh MacIntyre for managing the full IBIS database.
This work was supported by grants from the National Institutes of Health, K01 MH103594
(Eggebrecht), R01 MH093510 (Pruett), an NIH Autism Centers of Excellence Network grant,
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R01 HD055741 (Piven), Autism Speaks #6020 (Piven), the Simons Foundation #140209 (Piven), K01 MH101653 (Wolff), P30 NS098577 (Snyder), U54 Intellectual and Developmental
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Disabilities Research Centers (IDDRC; HD079124 to UNC (Piven), HD087011 to Washington University (Constantino and Schlaggar), HD86984 to CHOP (Schultz), HD083091 to UW (Estes)), and the McDonnell Center for Systems Neuroscience. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National
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Institutes of Health.
Competing interests: The authors declare the following conflicts of interests: R.C.M. receives acting, modeling, and speaking fees from Siemens Healthcare; A.C.E. is a founder and a member
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of the Board of Directors of Biospective Inc. All other authors declare no biomedical financial interests or potential conflicts of interest.
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Author contributions: Conceptualization, J.R.P. , J.J.W, J.T.E.., and J.P.; Data Curation, J.T.E., J.J.W., A.M.E., L.Z., C.M.A., A.Z.S, K.N.B., M.A.S., and G.G.; Formal Analysis, C.J.M., A.T.E. and A.T.; Investigation, C.J.M., A.T.E., J.J.W., J.T.E., A.T., and J.R.P.; Methodology, C.J.M., A.T.E, S.E.P., A.T., J.R.P., and B.L.S.; Project Administration, J.R.P., J.T.E., J.P., K.N.B., L.Z., A.C.E., H.C.H., S.J.P., R.T.S., A.M.E., and M.A.S.; Resources, K.N.B., A.C.E., H.C.H., S.J.P., R.T.S., J.P., and J.R.P.; Software, C.M. A.T.E., A.T., and J.R.P.; Supervision,
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K.N.B., A.C.E., H.C.H., S.J.P., R.T.S., J.P., S.E.P., and J.R.P.; Visualization, C.J.M., and A.T.E.; Writing – Original Draft, C.J.M., A.T.E., J.T.E., J.R.P.; Writing – Review and Editing, C.J.M., A.T.E., J.T.E., A.T., J.J.W., C.M.A., A.M.E., L.Z., K.N.B., R.C.M., N.M., H.C.H.,
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S.J.P., R.T.S., M.A.S., G.G., B.L.S., S.E.P., J.P., and J.R.P.; Funding Acquisition, J.P., J.R.P., A.T.E., and J.J.W.
∆ The Infant Brain Imaging Study (IBIS) Network is an NIH funded Autism Centers of
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Excellence project and consists of a consortium of eight universities in the U.S. and Canada.
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Clinical Sites: University of North Carolina: J. Piven (IBIS Network PI), H.C. Hazlett, C. Chappell; University of Washington: S. Dager, A. Estes, D. Shaw; Washington University: K. Botteron, R. McKinstry, J. Constantino, J. Pruett; Children’s Hospital of Philadelphia: R. Schultz, J. Pandey; University of Alberta: L. Zwaigenbaum; University of Minnesota: J. Elison; Data Coordinating Center: Montreal Neurological Institute: A.C. Evans, D.L. Collins, G.B. Pike,
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V. Fonov, P. Kostopoulos; S. Das; Image Processing Core: New York University: G. Gerig; University of North Carolina: M. Styner; Statistical Analysis Core: University of North Carolina:
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H. Gu.
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Figure Legends
Figure 1. Restricted and repetitive behaviors and functional connectivity in infants
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The number of items endorsed for each RBS-R factor at both 12 months old (mo; blue) and 24 mo (red): (A) the restricted behavior factor includes four items pertaining to limited range of focus, interest, or activity (e.g., preoccupation with part of object); (B) the stereotyped behavior factor includes six items relating to repeated, purposeless movements (e.g., arm flapping); (C) the self-injurious factor includes eight items relating to repeated actions that can cause injury to
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the body (e.g., hair pulling); (D) the ritualistic/sameness factor includes seventeen items relating to performing activities of daily living in a similar manner or resistance to change (e.g., arranging/ordering). Also see Supplemental Table S1. (E) An Infomap-sorted mean fcMRI matrix derived from the correlation structure between 230 functionally-defined regions of
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interest (ROIs). See Supplemental Figure S1. (F) Left lateral view of the ROIs on the brain surface, colored according to network assignment (see Materials and Methods for details and definition of network abbreviations). For clarity, ROIs in the cerebellum are displayed without
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the cerebellar structure.
Figure 2. Connections showing a strong relationship to restricted behavior are concentrated in temporal default mode—dorsal attention and temporal default mode— posterior frontoparietal control network pairs at 24 months. (A) Strong positive and negative brain-behavior relationships cluster by sign, within a subset of network blocks (note the visually striking red clustering at 24 mo but not at 12 mo indicated by the arrow). (B) Functional connections showing a strong relationship to restricted behavior are defined as those with p ≤ 27
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0.05. Quantifying the level of clustering with enrichment analyses (see Materials and Methods and Supplemental Figure S2) reveals that strong brain-behavior relationships are constrained to a minority of network pairs that differ across age (enriched network pairs outlined in black). (C) Only two functional network pairs significantly enriched at either age (
12 mo;
24 mo) also
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exhibit significant differences across age groups ( ● ; tested via McNemar χ2): tDMN-DAN and tDMN-pFPC, (see Supplemental Table S3). (D) For each primary result, ROI pairs contributing to enrichment are visualized on a surface representation of the cortex. Ball color denotes
functional network membership and line color joining ROI pairs denotes the sign of brain-
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behavior relationship (red-positive; blue-negative). The signs of brain-behavior relationships are largely consistent within network pairs. See Supplemental Figure S2 for more detailed analysis
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of the fc underlying these brain-behavior relationships.
Figure 3. Strong fc-stereotyped relationships cluster within temporal default mode—visual network connections at 12 months and dorsal attention—subcortex and temporal default mode—posterior frontoparietal control network connections at 24 months. Analyses are as outlined in Figure 2. (A) Negative binomial regression based relationships between fc and
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stereotyped behavior scores. (B) Significant clustering of strong brain-behavior associations (p ≤ 0.05) is restricted to a subset of network pairs (outlined in black). (C) Primary findings include tDMN-Vis enrichment at 12 mo (but not 24 mo) and DAN-SubCtx and tDMN-pFPC enrichment at 24 mo (but not 12 mo;). See Supplemental Figure S3 for enrichment and McNemar (age
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group comparison) analyses. See Supplemental Table S3 for statistics. (D) At 12 mo, the VistDMN network pair showed primarily negative fc-stereotyped relationships while at 24 mo both
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network pairs showed primarily positive fc-stereotyped relationships. See Supplemental Figure S3 for more detailed analysis of the fc underlying these brain-behavior relationships.
Figure 4. Strong fc-ritualistic/sameness relationships cluster within visual-temporal default mode network connections at 12 months and visual—posterior frontoparietal control network connections at 12 and 24 months. Analyses follow those outlined in Figure 2. (A) Note the particularly visually striking cluster of negative fc-ritualistic/sameness behavior relationships in the Vis-tDMN network pair at 12 mo. (B) Significant clustering of strong brainbehavior associations (p ≤ 0.05) is restricted to a subset of network pairs. (C) Primary findings 28
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included tDMN-Vis enrichment at 12 mo (but not 24 mo; indicated with dot in a blue box) and Vis-pFPC pair enrichment at both 12 mo and 24 mo (indicated with a green box). See Supplemental Figure S4 for McNemar (age group differences) analysis and Supplemental Table S3 for statistics. (D) Both Vis-tDMN (at 12 mo) and Vis-pFPC (at 12 mo and 24 mo)
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showed primarily negative fc-stereotyped relationships: higher ritualistic and sameness scores are associated with less positive fc between Vis and both tDMN and pFPC. See Supplemental Figure S4 for more detailed analysis of the fc underlying these brain-behavior relationships.
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Figure 5. Global patterns of RRB-fc relationships across age and behavior. The first column depicts the nature of the brain-behavior relationship, that is, whether the relationship is positive
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or negative and whether it spans primarily positive or negative fc values. The second column contains the network pair in question. The third column emphasizes whether greater behavior scores were associated with increasing or decreasing fc between the network pair. The last three columns catalogue implicated behavior and time point combinations for the given network pairs. See Supplemental Figure S5 for more detailed analysis of the self-injurious RBS-R subcategory. See Supplemental Figure S6 for the global RRB-fc relationships as derived using
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adult-based functional networks.
Figure 6. ROIs contributing to enrichment across behavior factors. Analyses revealed two network pairs, one at 12 mo and one at 24 mo, implicated with multiple behaviors. (A)
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Functional connections between Vis and tDMN that showed a strong relationship to both stereotyped and ritualistic/sameness behavior at 12 mo. (B) Functional connections between the
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pFPC-tDMN that showed a strong relationship to both restricted and stereotyped behavior at 24 mo are visualized on the brain. As above, the color of the ROI denotes the functional network, and the color of the connecting bar denotes the sign of the brain-behavior relationship.
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Tables: Table 1. Demographics 24-month age group
Number participants with data available at both time points (n)
467 135 118
379 107 87
327 48 38
31 76 11 74 12.5 (0.5) 98.0(18.2)
20 56 11 50 24.6 (0.6) 99.8(20.8)
12 24 2 22 -
0.3 (0.8)
0.4 (0.9)
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Stereotyped 0.6(1.2) Ritualistic + Sameness 0.6 (1.6) Self-Injurious 0.4 (0.8)
0.7 (1.3) 1.4 (1.9) 0.5 (1.1)
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RBS-R Low motion fcMRI Botha a Outcome group LRHRHR+ a Number of Males Age in monthsa Mean Mullen Early Learning Composite score (SD)a Mean RBS-R score (SD)a Restricted
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12-month age group
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Numbers reflect the group of participants providing both RBS-R and low motion fcMRI data RBS-R: Repetitive Behavior Scale-Revised fcMRI: functional connectivity: Magnetic Resonance Imaging LR-: Low-risk, negative ASD diagnosis HR-: High-risk, negative ASD diagnosis HR+: High-risk, positive ASD diagnosis SD: Standard deviation
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Restricted and Repetitive Behavior and Brain Functional Connectivity in Infants at Risk for Developing Autism Spectrum Disorder
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Supplemental Information Exclusion criteria for participants
Exclusion criteria included: known genetic conditions; gestational age < 36 weeks or birth weight
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< 2000 g; maternal substance abuse during pregnancy; contraindication for MRI; a first degree relative with psychosis, schizophrenia, or bipolar disorder; significant medical or neurological
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conditions affecting growth, development, or cognition; non-English-speaking family; adoption; or twin birth.
Effect of clinical assessment site and age in months on RBS-R score
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The potential effect of site on RBS-R scores was tested using a 1-way ANOVA. The potential effect of age on RRB score within each age group was examined with Spearman’s correlation. There was no effect of site on RRB scores, and age-in-months was not correlated with RRB score
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behaviors tested).
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(Supplemental Table S2, alpha level of 0.0125 reflecting Bonferroni correction for the four
Image acquisition
The protocol included T1-weighted and T2-weighted anatomical imaging, and resting-state fcMRI. The present study utilized the 3-D sagittal T2-weighted sequence (TE = 497 ms, TR = 3200 ms, matrix 256 x 256 x 160, 1 mm3 voxels) for anatomical segmentation, and spatial normalization procedures (1-3). Functional images were acquired using a gradient echo planar sequence, sensitive to changes in T2* BOLD signal (TE = 27 ms, TR = 2500 ms, voxel size 4 mm
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isotropic, flip angle 90 degrees, field of view 256 mm, matrix 64 x 64, pixel bandwidth 1,906 Hz). Atlas transformation of the functional data was computed through a sequence of affine transforms. First, T2-weighted images were registered to age-specific (12 and 24 months) MRI atlas targets to
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account for morphological changes across development (4). fMRI data were then aligned to the atlas registered T2-weighted images. After fMRI–to–T2-weighted atlas transform composition, the volumetric time series were resampled in atlas space (3 mm voxels) including correction for
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head movement in a single resampling step. Finally, each atlas-transformed functional dataset was
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visually inspected in sagittal, transverse, and coronal views to ensure proper spatial alignment.
fMRI preprocessing
Following previous work (1, 5), initial fMRI data preprocessing included: (i) sinc interpolation to compensate for slice dependent time shifts, (ii) correction of systematic odd-even slice intensity
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differences caused by interleaved acquisition, (iii) spatial realignment to account for head motion both within and across fMRI runs, and (iv) within-run intensity normalization to a whole brain mode value of 1000 (6). Data were then subjected to rigorous frame censoring (“scrubbing”) based
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on the frame-to-frame displacement (FD) measure (7), which sums 3 absolute translations (X, Y, Z) and 3 absolute rotations (Pitch, Yaw, Roll) evaluated at a radius of 50 mm. Frames with FD
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≥0.2 mm were marked for subsequent censoring. Temporally isolated (5 or fewer contiguous) FD <0.2 mm frames were also censored. fMRI runs with fewer than 30 uncensored frames were discarded. After frame censoring, epochs of data containing six or more contiguous uncensored frames were used for further analyses. The fMRI runs with fewer than 30 uncensored frames were discarded. No significant relationship was observed in the percentage of scrubbed volumes with regard to age (Mann-Whitney U-test: n=118, z=0.92, p=0.36). To control for potential biases
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attributable to the amount of data per cohort, 150 non-censored (retained) fMRI frames were used, prioritizing runs with the most retained frames, for correlation analysis in each subject.
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Definition of nuisance regressors and regions of “non-interest” in atlas space
The nuisance regressors included (i) three translation and three rotation time series derived by retrospective head motion correction, together with expansion terms (8), and (ii) time series
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derived from regions of non-interest (whole brain, white matter, and cerebrospinal fluid) and their 1st derivatives. White matter and CSF regions were manually defined in atlas-transformed T1-
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weighted images, from 15 representative subjects in each age group (including data from both high- and low-risk individuals). To reduce the risk of intruding on gray matter, these regions of noninterest were eroded away from boundaries with other tissues using a 2.5 mm Gaussian blurring kernel. The intersection over each age group was computed to create the final white matter and
Definition of ROIs
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CSF regions of noninterest.
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Briefly, 280 initial regions of interest (ROIs) were defined from a combination of meta-analyses of autism studies (9) and of task data and cortical functional areal parcellations (10) obtained in
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typical subjects (11). After ROI placements were inspected on structural anatomy registered to age-specific atlas templates, 50 ROIs were removed because they were partially located outside of the whole brain mask or showed inconsistent gray matter coverage across ages.
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Derivation of putative functional networks in infants and toddlers We generated a cross-age network model using the mean fcMRI matrix from a longitudinal cohort of N=48 children with clean fcMRI data at both 12-and 24-months (Supplemental Figure S1).
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Functional network derivation closely followed previously published procedures (12). The full set of ROI-pair correlations (Fisher-z values) was averaged across subjects producing a 230 x 230 connection matrix (Fig. 1E; nodes=ROIs, edges=correlation coefficients). The 230 ROIs were then
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separated into functional networks (Fig. 1F) using the Infomap community detection algorithm(13) that assigns ROIs to communities based on the maximization of within-module random walks in
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the adjacency matrix derived from the thresholded and binarized connection matrix. Infomap solutions were generated for each of 91 edge density thresholds (1-10% in steps of 0.1%). An automated consensus procedure generated a single model of the community structure, or equivalently network structure, by first combining Infomap solutions across edge density
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thresholds by maximizing the normalized mutual information of groups of neighboring solutions and then maximizing modularity (3). Only Infomap solutions for graphs with the largest component containing more than 95% of the nodes were included in the consensus procedure.
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Communities containing fewer than five ROIs were combined and defined as a collection denoted as unspecified ROIs (US). As we were interested in brain-behavior interactions at the systems
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network level, unspecified ROIs were omitted from the current analysis.
Enrichment analysis details These data-driven statistical methods, adapted from genome-wide association studies (14-16) were used to determine whether strong (defined as p ≤ 0.05, uncorrected; e.g., Figure 2B) brainbehavior relationship values (regression coefficients between RBS and fc for each ROI-pair) were
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significantly clustered within specific network pairs, rather than uniformly distributed throughout all network pairs. A 2-sided χ2(df=1) test and a complementary hypergeometric test (e.g., Supplemental Figure S2) were calculated on the number of observed strong (p ≤ 0.05) brain-
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behavior relationship values within each network pair, with the null hypothesis being that the number of strong values is uniformly distributed across all possible network pairs. High χ2 values indicate that the number of strong brain-behavior relationships within each network pair is
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enriched (or depleted), that is much greater than (or much less) than the number expected by chance, respectively. As we were only interested in those network pairs that showed enrichment,
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data-driven 1-tailed p-values were calculated from permutation testing, as described below, by setting the p-values of ROI pairs showing depletion to 1. To complement the χ2 statistics, the hypergeometric statistic was also calculated. This test assesses the likelihood of observing the given number of strong relationships in a network pair, given the total number of strong
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relationships observed, the total number of ROI-pairs, and the number of ROI-pairs within the network pair of interest. Enriched network pairs were those that met significance, as determined by permutation testing (see below), for both the χ2 and hypergeometric tests. The McNemar χ2
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statistic was used to assess whether there was a statistically significant difference in the proportions of discordant tests between two ages.
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For each of the χ2, hypergeometric, and McNemar tests, empirical significance levels—
reflecting the 5% false positive rate for enrichment—were determined using permutation testing where the fcMRI-RBS-R data pairing was randomized for each of 10,000 iterations. The set of permutation-based false statistics from all of the network blocks represented a brain-wide null distribution for statistical evaluation of the data. The relative locations of the true data statistics in these distributions provided the reported permutation-based false positive rate p-values and
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reflected the empirical probability of calculating the actual observed statistical value (χ2, hypergeometric, and McNemar) for any network pair in the permutation. In this way, permutation testing provided control over family wise error rate without the need to make assumptions about
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the shape or parameters of the underlying population distribution (14). The randomization preserved the correlation and missing data patterns in the data (e.g., infants assessed at 12 months only). At each iteration, permutations of complete fcMRI matrices were conducted separately on
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the 12-month only group, the 24-month only group, and for the set of data at both time points. Longitudinal subjects were then recombined with cross-sectional subjects producing two separate
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brain-wide null distributions — one for each time point.
Enrichment analyses using adult network structure
Complementary enrichment analyses were also performed using a brain functional network
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structure derived from a previously published independent fcMRI dataset of typical adults (11) (Supplemental Figure S1). While these analyses revealed highly similar patterns of network level brain-behavior relationships (see Supplemental Figure S6 for overall pattern), fewer network
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Supplemental Figure S1. Defining infant-toddler brain functional network structure. Related to Figure 1. (A) The 230 ROIs used in the current study, sorted according to a consensus Infomap model of infant/toddler network structure using fcMRI data from 48 children with fcMRI data at both the 12 mo and 24 mo time points (network abbreviations given in Materials and Methods). (B) Adapted model of adult functional networks (11).
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Supplemental Figure S2. Restricted Enrichment Details. Related to Figure 2. (A) Enrichment analyses were used to quantify the relative density of connections showing strong brain-behavior relationships (black dots indicate uncorrected negative binomial regression coefficient pvalues<=0.05) within specific network pairs. This was assessed using complementary (B) chisquare and (C) hypergeometric statistics. Network blocks are colored based on p-values via a permutation-based, experiment-wide, false positive rate. Significance is indicated with a dot and additionally reflects multiple comparison correction for four behaviors tested. (D) A McNemar test evaluated differences in brain-behavior associations between age groups. (E) tDMN-DAN and tDMN-pFPC showed both significant enrichment at 24 months and significant differences between
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age groups. (F) For each network pair constituting a primary result, functional connections are colored according to both 1) the sign of the relationship between fc value and behavior (light blue to dark blue to magenta colors reflect negative brain-behavior relationships, and green to yellow to red colors reflect positive brain-behavior relationships) and 2) the proportion of individual fcMRI values above zero (light blue and green denote that – across subjects – the ROI pair contains only negative fcMRI values while magenta and red reflect ROI pairs with only positive fcMRI values). The proportion of fcMRI data that are positive for each brain-behavior relationship is also displayed in a scatter plot.
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Supplemental Figure S3. Stereotyped Enrichment Details. Related to Figure 3. (A-D) Format similar to Supplemental Figure S2. (E) Three network pairs showed both significant enrichment at a given age and significant differences across age groups: Vis-tDMN at 12 mo and tDMN-pFPC and SubCtx-DAN at 24 mo. (F) Format similar to Supplemental Figure S2.
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Supplemental Figure S4. Ritualistic/Sameness Enrichment Details. Related to Figure 4. (AD) Format similar to Supplemental Figure S2. (E) The Vis-tDMN network pairs showed both significant enrichment at 12 mo and significant differences across age groups while the Vis-pFPC network pair showed significant enrichment at both 12 mo and 24 mo. (F) Format similar to Supplemental Figure S2.
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Supplemental Figure S5. No network level results for strong fc-self-injurious associations. (A) Analyses follow those outlined in Figure 2 and Supplemental Figure S2. (A-B) Matrices describe the relationships between fc and self-injurious behavior scores. (C) The Vis-DAN network pair showed significant clustering of strong (p ≤ 0.05) negative brain-behavior relationships at 24 mo but not at 12 mo, as assessed with complementary chi-square and hypergeometric statistics. (For brevity, only the hypergeometric values are shown here.) (D) A McNemar test evaluated differences in brain-behavior associations between age groups. (E) No network pairs showed both significant enrichment at a given age and significant differences across age groups.
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Supplemental Figure S6. Brain-behavior analyses using adult functional networks. ROIs were grouped based on an adult functional network structure. These analyses implicated a highly similar subset of functional network pairs but resulted in fewer primary results.
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Supplementary Table S1. RBS-R factors (17) RBS-R Definition items Restricted 40-43 Limited range of focus, interest or activity Apparently purposeless movements or actions that are repeated in a similar Stereotyped 1-6 manner Ritualistic/ Performing activities of daily living in a similar manner, 23-39 Sameness or resistance to change and insistence that things stay the same Movement or actions that have the potential to cause redness, bruising, or Self-Injurious 7-14 other injury to the body, and that are repeated in a similar manner
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Supplementary Table S2. Effect of site and age in months on RBS-R score
Stereotyped Self-Injurious
0.29 (0.79) 0.44 (0.86) 0.64 (1.2) 0.72 (1.3) 0.40 (0.80) 0.54 (1.1) 0.61 (1.6) 1.4 (1.9)
Effect of site
F 1.3 8.8x10-1 8.8x10-1 3.7x10-2 2.5 6.2x10-1 2.4 6.9x10-1
p 2.7x10-1 4.5x10-1 4.5x10-1 9.9x10-1 5.8x10-2 6.0x10-1 6.4x10-2 5.6x10-1
Correlation with age in months rho 8.3x10-3 8.7x10-2 6.0x10-2 -3.1x10-2 7.9x10-2 -2.4x10-2 7.7x10-2 -3.9x10-2
p 8.5x10-1 8.4x10-2 1.9x10-1 5.5x10-1 8.4x10-2 6.3x10-1 9.1x10-2 4.4x10-1
AC C
EP
Ritualistic/ Sameness
12 24 12 24 12 24 12 24
RBS-R score (SD)
M AN U
Restricted
Age Group (mo)
TE D
Factor
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Table S3. Significant network pair statistics HG asymp. p
HG FPR p
MN χ2
MN asymp. p
MN FDR p
1.5x10-25
1.2x10-3
1.3x10-17
9.7x10-4
14.1
8.8x10-5
4.7x10-2
4.0x10-15
4.6x10-3
5.6x10-11
4.3x10-3
2.57
5.4x10-2
3.3x10-1
1.7x10-39
4.9x10-4
1.5x10-30
2.1x10-4
68.6
5.9x10-17
2.3x10-4
2.7x10-19
3.3x10-3
2.5x10-14
2.8x10-3
32.1
7.2x10-9
5.3x10-3
1.9x10-13
6.4x10-3
3.3x10-9
7.6x10-3
0.86
1.8x10-1
5.2x10-1
<1.0x10-33
1.1x10-6
8.6x10-39
4.3x10-5
50.4
6.1x10-13
8.1x10-4
3.1x10-27
1.1x10-3
1.8x10-16
1.4x10-3
12.2
2.3x10-4
5.5x10-2
8.7x10-18
3.3x10-3
5.0x10-12
3.6x10-3
5.12
1.2x10-2
1.8x10-1
6.1x10-20
2.9x10-3
1.2x10-13
3.1x10-3
24.6
3.5x10-7
1.1x10-2
2.3x10-18
3.5x10-3
5.7x10-14
2.9x10-3
28.7
4.3x10-8
6.7x10-3
3.7x10-36
6.6x10-4
1.9x10-23
9.2x10-4
20.8
2.6x10-6
5.6x10-2
<1.0x10-33
1.1x10-6
<1.0x10-33
1.1x10-6
165
5.1x10-38
1.9x10-5
1.3x10-11
1.1x10-2
5.5x10-10
9.8x10-3
23.8
5.2x10-7
4.3x10-2
SC
RI PT
χ2 FPR p
158
12
757
12
45.8
12
95.8
1.3x10-22
2.3x10-3
5.6x10-18
2.0x10-3
28.2
5.3x10-8
3.0x10-2
12
71.7
2.5x10-17
4.4x10-3
1.9x10-13
4.5x10-3
3.4
3.3x10-2
3.9x10-1
44.5
2.5x10-11
1.1x10-2
3.9x10-9
1.1x10-2
3.4
3.3x10-2
3.9x10-1
tDMN24 145 pFPC Self-injurious behavior
2.6x10-33
8.8x10-4
1.8x10-22
8.3x10-4
20.3
3.3x10-6
5.9x10-2
Vis-DAN
6.6x10-22
2.7x10-3
5.8x10-18
1.9x10-3
24.3
4.0x10-7
2.4x10-2
Vis-pFPC Vis-pFPC
EP
VistDMN VispcDMN pcDMNtDMN
TE D
12
AC C
Vis-Vis
χ2 asymp. p
M AN U
Age χ2 group statistic (mo) Restricted behavior Vis12 109 tDMN Vis12 61.7 SMN2 tDMN24 173 DAN tDMN24 80.6 pFPC Stereotyped behavior Vis-Vis 12 54.1 Vis12 321 tDMN Vis-pFPC 12 117 pFPC12 73.8 pcDMN pFPC24 83.6 tDMN SubCtx24 76.4 DAN Ritualistic/sameness behavior Network pair
24
24
92.6
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Supplemental References
AC C
EP
TE D
M AN U
SC
RI PT
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