Sleep Problems in Preschoolers With Autism Spectrum Disorder Are Associated With Sensory Sensitivities and Thalamocortical Overconnectivity

  • Annika Carola Linke
    Correspondence
    Address correspondence to Annika Carola Linke, Ph.D.
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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  • Bosi Chen
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California

    San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
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  • Lindsay Olson
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California

    San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
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  • Cynthia Ibarra
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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  • Chris Fong
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California

    San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
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  • Sarah Reynolds
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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  • Michael Apostol
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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  • Mikaela Kinnear
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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  • Ralph-Axel Müller
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California

    San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California

    SDSU Center for Autism and Developmental Disorders, San Diego, California
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  • Inna Fishman
    Affiliations
    Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California

    San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California

    SDSU Center for Autism and Developmental Disorders, San Diego, California
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Open AccessPublished:August 02, 2021DOI:https://doi.org/10.1016/j.bpsc.2021.07.008

      Abstract

      Background

      Projections between the thalamus and sensory cortices are established early in development and play an important role in regulating sleep as well as in relaying sensory information to the cortex. Atypical thalamocortical functional connectivity frequently observed in children with autism spectrum disorder (ASD) might therefore be linked to sensory and sleep problems common in ASD.

      Methods

      Here, we investigated the relationship between auditory-thalamic functional connectivity measured during natural sleep functional magnetic resonance imaging, sleep problems, and sound sensitivities in 70 toddlers and preschoolers (1.5–5 years old) with ASD compared with a matched group of 46 typically developing children.

      Results

      In children with ASD, sleep problems and sensory sensitivities were positively correlated, and increased sleep latency was associated with overconnectivity between the thalamus and auditory cortex in a subsample with high-quality magnetic resonance imaging data (n = 29). In addition, auditory cortex blood oxygen level–dependent signal amplitude was elevated in children with ASD, potentially reflecting reduced sensory gating or a lack of auditory habituation during natural sleep.

      Conclusions

      These findings indicate that atypical thalamocortical functional connectivity can be detected early in development and may play a crucial role in sleep problems and sensory sensitivities in ASD.

      Keywords

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      • Postorino V.
      • Siracusano M.
      • Riccioni A.
      • Curatolo P.
      The relationship between sleep problems, neurobiological alterations, core symptoms of autism spectrum disorder, and psychiatric comorbidities.
      ,
      • Tzischinsky O.
      • Meiri G.
      • Manelis L.
      • Bar-Sinai A.
      • Flusser H.
      • Michaelovski A.
      • et al.
      Sleep disturbances are associated with specific sensory sensitivities in children with autism.
      ). Together, these findings point toward disrupted thalamocortical connectivity as a possible mechanism underlying both sleep disturbances and sensory sensitivities in ASD. This study, therefore, investigated how thalamocortical connectivity, sleep problems, and atypical sensory processing relate in a cohort of toddlers and preschoolers with ASD.
      We first aimed to examine links between sleep problems and sensory sensitivities in 15- to 65-month-old toddlers and preschoolers with ASD compared with an age-matched typically developing (TD) control group. Next, we assessed whether atypical FC (estimated from fMRI acquired during natural sleep) between the thalamus and auditory cortices could be observed in this age range and whether it was related to sleep problems and sensory sensitivities. Given the early development of connections between the thalamus and auditory cortex, we hypothesized that FC would be increased in preschoolers with ASD and that the strength of thalamocortical FC would be related to sleep problems and sensory sensitivities. Finally, we quantified the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) (
      • Zou Q.H.
      • Zhu C.Z.
      • Yang Y.
      • Zuo X.N.
      • Long X.Y.
      • Cao Q.J.
      • et al.
      An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF.
      ,
      • Zuo X.N.
      • Di Martino A.
      • Kelly C.
      • Shehzad Z.E.
      • Gee D.G.
      • Klein D.F.
      • et al.
      The oscillating brain: Complex and reliable.
      ) in the primary auditory cortex and thalamus during resting-state fMRI. This index provides a measure of blood oxygen level–dependent (BOLD) activity, which has been observed to decrease in auditory regions but increase in the thalamus during sleep in healthy young adults (
      • Del Felice A.
      • Formaggio E.
      • Storti S.F.
      • Fiaschi A.
      • Manganotti P.
      The gating role of the thalamus to protect sleep: An f-MRI report.
      ). We hypothesized BOLD amplitude to be increased in the auditory cortex but decreased in the thalamus in children with ASD, potentially reflecting amplified sound processing and reduced thalamocortical gating, as has been observed in other neurodevelopmental disorders [e.g., schizophrenia (
      • Freedman R.
      • Olsen-Dufour A.M.
      • Olincy A.
      Consortium on the Genetics of Schizophrenia
      P50 inhibitory sensory gating in schizophrenia: Analysis of recent studies.
      )].

      Methods and Materials

       Participants

      A total of 70 young children with ASD and 46 TD children, ages 15 to 65 months, were enrolled in an ongoing longitudinal study of early brain markers of autism. Children with a (suspected) diagnosis of autism were referred from specialty autism clinics, state-funded early education and developmental evaluation programs, local pediatricians, service providers, and community clinics; TD children were recruited from the community. All participants were safety-screened for MRI contraindications (see the Supplement for exclusionary criteria).
      Consent was acquired from caregivers, and families were compensated for their time. The research protocol was approved by the institutional review boards of the University of California San Diego and San Diego State University. MRI data were acquired during natural sleep, and after quality assessments, final fMRI analyses included 29 participants with ASD and 30 TD participants (see the Supplement). The groups did not differ on age, sex, or head motion during the fMRI scans. Demographics for all participants included in analyses are summarized in Table 1 and Table S1.
      Table 1Demographics for Full and fMRI Cohorts
      DemographicsFull CohortfMRI Cohort
      ASD, n = 70TD, n = 46StatisticASD, n = 29TD, n = 30Statistic
      Gestational Age at Birth, Weeks38.4 ± 2.6 [31–43]39.7 ± 1.1 [37–42]t103 = 3.1; p < .0538.5 ± 2.5 [31–43]39.5 ± 1.2 [37–42]t54 = 1.84; p = .07
      Age at Behavioral Assessment, Months34.7 ± 12.4 [17–64]32.6 ± 15.2 [15–64]t114 = −0.82; p = .4233.3 ± 9.8 [17–54]34.1 ± 14.3 [15–64]t57 = 0.25; p = .81
      Age at MRI Scan, MonthsN/AN/AN/A34.7 ± 10.1 [18–56]34.5 ± 14.8 [16–65]t57 = −0.05; p = .96
      Sex, Female, n (%)16 (23%)21 (46%)χ2 = 6.58; p = .019 (31%)13 (43%)χ2 = 0.96; p = .33
      ADOS-2 SA11.8 ± 4.6 [3–20]N/AN/A12.0 ± 4.9 [3–19]N/AN/A
      ADOS-2 RRB3.3 ± 2.1 [0–8]N/AN/A3.5 ± 2.2 [0–8]N/AN/A
      ADOS-2 Total15.0 ± 5.5 [4–26]N/AN/A15.5 ± 5.7 [6–26]N/AN/A
      RMSD Run1N/AN/AN/A0.12 ± 0.04 [0.047–0.188]0.11 ± 0.04 [0.046–0.172]t57 = −1.04; p = .30
      RMSD Run2N/AN/AN/A0.12 ± 0.045 [0.046–0.24]0.10 ± 0.03 [0.05–0.176]t57 = −1.18; p = .24
      Values are presented as mean ± SD [range] unless otherwise indicated. The ASD and TD groups were matched on age, sex, and in-scanner head motion (RMSD). Information on exact gestational age at birth was missing for 8 children with ASD (2 of 8 known to be born at term) and 3 TD children; this includes 2 ASD children (1 known to be born at term) and 1 TD child in the fMRI cohort.
      ADOS-2, Autism Diagnostic Observation Schedule, Second Edition; ASD, autism spectrum disorder; fMRI, functional magnetic resonance imaging; N/A, not applicable; RMSD, root-mean-square deviation; RRB, restricted and repetitive Behavior; SA, social affect; TD, typically developing.

       Diagnostic and Behavioral Assessments

      All participants in the ASD group received a diagnosis of ASD [or clinical best estimate in children younger than age 3 years (
      • Ozonoff S.
      • Young G.S.
      • Landa R.J.
      • Brian J.
      • Bryson S.
      • Charman T.
      • et al.
      Diagnostic stability in young children at risk for autism spectrum disorder: A baby siblings research consortium study.
      )], based on the DSM-5 criteria (
      American Psychiatric Association, DSM-5 Task Force
      Diagnostic and Statistical Manual of Mental Disorders: DSM-5™ (5th ed).
      ), supported by the Autism Diagnostic Observation Schedule, Second Edition (
      • Lord C.
      • Rutter M.
      • DiLavore P.
      • Risi S.
      • Gotham K.
      • Bishop S.L.
      Autism Diagnostic Observation Schedule.
      ) (Table 1), administered by research-reliable clinicians; the Autism Diagnostic Interview-Revised (for children older than 36 months); and expert clinical judgment. Parents also completed the Social Communication Questionnaire (current form) (
      • Rutter M.
      • Lord C.
      • Bailey A.
      The Social Communication Questionnaire.
      ), a screener for ASD, with no TD participants exceeding the cut-off score of 15 (all TD scores ≤ 10). Measures summarizing sleep problems were obtained from the Child Behavior Checklist (CBCL) for ages 1.5 to 5 years (
      • Achenbach T.M.
      • Rescoria L.A.
      Manual for the ASEBA Preschool Forms & Profiles.
      ) and from an in-house Sleep Questionnaire. The CBCL Sleep Problems T score, the 6 individual items it is derived from (
      • Gregory A.M.
      • Cousins J.C.
      • Forbes E.E.
      • Trubnick L.
      • Ryan N.D.
      • Axelson D.A.
      • et al.
      Sleep items in the child behavior checklist: A comparison with sleep diaries, actigraphy, and polysomnography.
      ), and 7 items from the Sleep Questionnaire were included in analyses (see the Supplement). To quantify sensory symptoms and, in particular, sound sensitivities, the Toddler or Child Sensory Profile 2 (
      • Dunn W.
      Sensory Profile 2.
      ) was administered to caregivers, and the Sensitivity Quadrant score and Auditory Processing score were used in analyses (with greater scores corresponding to greater impairment).
      ASD and TD group differences in the CBCL Sleep Problems T score, individual CBCL sleep items, Sleep Questionnaire items, and Sensory Profile scores were tested using independent samples two-tailed Welch’s t tests (to account for unequal variances) or likelihood ratio χ2 tests for the full cohort and separately for the subgroup of children with fMRI data. Given the restricted range of scores in the TD group, Pearson correlations were carried out to test for a relationship between sensory sensitivities (Sensory Profile Sensitivity Quadrant and Auditory Processing score) and sleep problems (CBCL Sleep Problems T score) in the ASD group only. Additional analyses were carried out to assess potential confounds of gestational age at birth, sex, and socioeconomic status and are presented in the Supplement.

       MRI Data

      Natural sleep MRI data (see the Supplement) were collected at the University of California San Diego Center for fMRI on a GE 3T Discovery MR750 (GE Healthcare) scanner using a Nova Medical 32-channel head coil. A multiband echo-planar imaging (EPI) sequence allowing simultaneous acquisition of multiple slices was used to acquire 2 fMRI datasets (6-min duration each) with high spatial and temporal resolution (repetition time = 800 ms, echo time = 35 ms, flip angle 52°, 72 slices, multiband acceleration factor 8, 2 mm isotropic voxel size, 104 × 104 matrix size, field of view 20.8 cm, 400 volumes per run). Two separate 20-second spin-echo EPI sequences with opposing phase encoding directions were also acquired using the same matrix size, field of view, and prescription to correct for susceptibility-induced distortions. After completion of the fMRI scans, a fast 3D spoiled gradient recalled T1-weighted sequence was used to acquire high-resolution structural images (0.8 mm isotropic voxel size, NEX = 1, echo time/inversion time = minimum full/1060 ms, flip angle 8°, field of view = 25.6 cm, matrix = 320 × 320, receiver bandwidth 31.25 Hz). Motion during structural acquisitions was corrected in real time using 3 navigator scans [real-time prospective motion correction (
      • White N.
      • Roddey C.
      • Shankaranarayanan A.
      • Han E.
      • Rettmann D.
      • Santos J.
      • et al.
      PROMO: Real-time prospective motion correction in MRI using image-based tracking.
      )], and images were bias corrected using the GE Pure (GE Healthcare) option.
      MRI data were preprocessed, denoised, and analyzed in MATLAB 2017b (The MathWorks, Inc.) using SPM12 (Wellcome Trust Centre for Neuroimaging, University College London), and the CONN toolbox v17f (NITRC). fMRI data were realigned, normalized to Montreal Neurological Institute template, bandpass filtered, and underwent nuisance regression to remove physiological noise and motion confounds (see the Supplement for detailed information on preprocessing and denoising procedures).

       fMRI Analyses

       Regions of Interest

      Analyses were carried out within left and right Heschl’s gyrus (HG) and left and right thalamus regions of interest (ROIs) extracted from the Harvard-Oxford atlas provided by FSL and the CONN toolbox. Additional FC analyses included all cortical Harvard-Oxford atlas ROIs to determine whether atypical thalamocortical connectivity was specific to auditory cortices (see below).

       FC Analysis

      BOLD time series were concatenated across the two EPI acquisitions and averaged across all voxels within each ROI. First, FC between the bilateral thalamus and HG was estimated using bivariate Pearson correlation standardized with a Fisher z-transform. Two-tailed independent samples t tests were used to assess differences in correlation magnitude between pairs of ROIs in the ASD compared with the TD group. Separate tests were also carried out for ipsilateral (average of intrahemispheric thalamus-HG) and contralateral (average of interhemispheric thalamus-HG) FC. FC (Fisher z) was Pearson correlated with age, separately in each group, to test for any age-related changes in auditory-thalamocortical connectivity. We hypothesized that auditory-thalamic FC would be elevated in toddlers and preschoolers with ASD. Results are reported at a threshold of p < .05 (Benjamini-Hochberg false discovery rate adjusted for multiple comparisons). To assess whether atypical thalamocortical connectivity was specific to sensory regions of the brain, a post hoc analysis also assessed whole-brain thalamocortical connectivity using the left and right thalamus as seeds and all Harvard-Oxford cortical ROIs as targets. Independent samples t tests of the Fisher z-transformed estimates of FC assessed differences between the ASD and TD groups. Finally, we assessed in an exploratory analysis whether atypical FC between HG and the thalamus was driven more strongly by specific regions within the thalamus involved in auditory processing (
      • Toulmin H.
      • Beckmann C.F.
      • O’Muircheartaigh J.
      • Ball G.
      • Nongena P.
      • Makropoulos A.
      • et al.
      Specialization and integration of functional thalamocortical connectivity in the human infant.
      ,
      • Kumar V.J.
      • van Oort E.
      • Scheffler K.
      • Beckmann C.F.
      • Grodd W.
      Functional anatomy of the human thalamus at rest.
      ,
      • Hwang K.
      • Bertolero M.A.
      • Liu W.B.
      • D’Esposito M.
      The human thalamus is an integrative hub for functional brain networks.
      ). BOLD time series within the left and right HG were Pearson correlated with the time series of every voxel within the thalamus ROIs, and t tests carried out for the Fisher z-transformed correlation coefficient of each thalamic voxel to test for ASD-TD differences. To rule out that children in the ASD and TD groups might have been scanned during different sleep stages, which could confound FC estimates, a number of additional analyses were conducted and are described in the Supplement. The influence of gestational age, sex, and socioeconomic status on auditory-thalamic FC were also assessed (see the Supplement).

       Fractional ALFF

      ALFF measures the power of the BOLD signal within a low-frequency range and is thought to reflect the amplitude of regional neural activity. ALFF was calculated as:
      ALFF=1Nt(h(t)BOLD(x,t)2)


      as implemented in the CONN toolbox, with N = the number of volumes, BOLD(x,t) = original BOLD time series before bandpass filtering, and h(t) = bandpass filter (see https://web.conn-toolbox.org/measures/other for more detail). fALFF was developed to better protect against noise and is a measure of the relative contribution of low-frequency fluctuations to the entire frequency range detectable by BOLD-optimized EPI (
      • Zou Q.H.
      • Zhu C.Z.
      • Yang Y.
      • Zuo X.N.
      • Long X.Y.
      • Cao Q.J.
      • et al.
      An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF.
      ,
      • Zuo X.N.
      • Di Martino A.
      • Kelly C.
      • Shehzad Z.E.
      • Gee D.G.
      • Klein D.F.
      • et al.
      The oscillating brain: Complex and reliable.
      ). It was calculated as the power within the low-frequency range (0.01–0.1 Hz) divided by the total power of the entire frequency spectrum, again using the implementation included with the CONN toolbox:
      fALFF=t(h(t)BOLD(x,t))2tBOLD(x,t)2


      ALFF and fALFF were calculated for each voxel in the brain and averaged for the left and right HG and thalamus ROIs. We hypothesized that f(ALFF) would be increased in HG but decreased in the thalamus in the ASD group, which was tested using independent samples two-tailed t tests. Additional analyses of covariance (controlling for root-mean-square deviation) tested for an association between f(ALFF) in HG and the thalamus and between the two regions and averaged HG-thalamus FC.

       Relationship Between FC, Sleep Problems, and Sensory Sensitivities

      Partial correlations (correcting for average head motion [root-mean-square deviation] and age) tested for a relationship between HG-thalamus FC and sensory sensitivities (Sensory Profile Sensitivity Quadrant and Auditory Processing score), the CBCL Sleep Problems T score, and those Sleep Questionnaire items that showed significant ASD-TD group differences in the fMRI cohort. To reduce the number of multiple comparisons, correlations were only conducted for contralateral and ipsilateral FC. Due to the narrow distribution of CBCL and Sensory Profile scores in the TD group, correlations where only assessed for children with ASD. We hypothesized that elevated FC between the thalamus and HG would be related to greater sensory sensitivity and more severe sleep problems in the ASD group. Given the relatively small sample size for detecting robust brain-behavior relationships, these results are presented as preliminary and uncorrected for multiple comparisons and need to be interpreted with caution.

      Results

       Sleep Problems in Preschoolers With ASD Are Associated With Sensory Sensitivities

      Sleep problems were significantly more pronounced in children with ASD compared with TD children (CBCL Sleep Problems T score, t98 = −3.82, p < .001) (Figure 1A), and those with ASD were reported to have more trouble sleeping (χ22 = 16.4, p < .001) and were more likely to resist bedtime (χ22 = 15.3, p < .001), to wake up at night (χ22 = 11.0, p = .004), and to be overtired (χ22 = 5.99, p = .05) and sleepless (χ22 = 12.03, p = .001). In addition, toddlers and preschoolers with ASD were reported to take significantly longer to fall asleep (sleep latency/time to fall asleep: ASD mean = 30.3 min [SD = 30.1], TD mean = 19.7 min [SD = 14.4], t109 = −2.2, p = .03) (see Table S2).
      Figure thumbnail gr1
      Figure 1Greater sleep problems and sensory sensitivity, including auditory sensitivity, in toddlers and preschoolers with autism spectrum disorder (ASD). (A, C) More severe sleep problems (as measured using the Child Behavior Checklist [CBCL] Sleep Problems scale, T scores) and prolonged sleep latency (time [in min] it takes a child to fall asleep, as reported on an in-house Sleep Questionnaire that asked caregivers about their child’s sleep habits in the past few weeks prior to participating in the study) are reported for young children with ASD compared with age- and sex-matched typically developing (TD) children, in (A) the full cohort and (C) the subgroup of children with successful functional magnetic resonance imaging (fMRI) scans (fMRI cohort). (B, D) Toddlers and preschoolers with ASD show greater sensory sensitivity and more severe auditory processing symptoms, as measured using the Sensory Profile 2 (T scores), in (B) the full cohort and (D) the subgroup of children with successful fMRI scans (fMRI cohort). These results remained significant when controlling for gestational age at birth and socioeconomic status, and no significant effects of sex (or sex × diagnosis interactions) were observed (see the ).
      As expected, the Sensory Sensitivities score and the Auditory Processing score from the Toddler or Child Sensory Profile 2 (
      • Dunn W.
      Sensory Profile 2.
      ) were significantly higher in the ASD than the TD group (p < .001) (Figure 1B). The CBCL Sleep Problems T score positively correlated with the Sensory Profile 2 Sensory Sensitivity quadrant in the ASD group (r = 0.35, p = .008, controlling for age) (Figure 2A; Table S2), with higher scores on each scale corresponding to greater impairment. This association remained significant when additionally controlling for ASD symptom severity (the Autism Diagnostic Observation Schedule, Second Edition total scores; r = 0.36, p = .007). The correlation with the Sensory Profile 2 Auditory Processing score was not significant (r = 0.12, p = .37, controlling for age) (Table S3).
      Figure thumbnail gr2
      Figure 2Correlations between sensory sensitivity, sleep problems, and Heschl’s gyrus (HG)–thalamus functional connectivity (FC). (A, B) The Sensory Profile Sensitivity score correlates positively (partial correlation controlling for age) with sleep problems as quantified using the Child Behavior Checklist (CBCL) Sleep Problems T score in the (A) full and (B) functional magnetic resonance imaging (fMRI)–only autism spectrum disorder groups. This association between sensory sensitivity and sleep problems remained significant when controlling for gestational age or socioeconomic status, with no sex differences observed (see the ). Correlations were not assessed for the typically developing group due to the narrow distribution of scores in typically developing children for these measures. (C) Sleep latency correlates positively (partial correlations controlling for in-scanner head motion [root-mean-square deviation] and age) with contralateral FC between the thalamus and HG in the autism spectrum disorder group. See the for additional analyses testing the effects of gestational age, socioeconomic status, sex, and sleep stage on HG-thalamus FC. All scatterplots show zero-order correlations. Robust linear regressions as implemented in MATLAB (2017b; The MathWorks, Inc.) with the robustfit function were conducted as post hoc analyses to minimize the potential influence of outliers. The association between the Sensory Profile Sensitivity score and CBCL Sleep Problems T score (controlling for age) remained significant in the full cohort (t = 2.04, p < .05; fMRI cohort t = 1.6, p = .12). Similarly, the relationship between sleep latency and contralateral HG-thalamus FC (controlling for age and root-mean-square deviation) remained significant (t = 2.5, p < .05; ipsilateral FC: t = 1.7, p = .1).
      Children with successful fMRI scans did not significantly differ on any of the included Sensory Profile or CBCL sleep measures from those without MRI in the ASD and TD groups (Table S4). As in the full cohort, children with ASD and successful fMRI scans had more sleep problems than TD peers as measured using the CBCL (Sleep Problems T score: t52 = −2.62, p = .012; trouble sleeping: χ22 = 9.62, p = .008; resists bedtime: χ22 = 9.73, p = .008; and, marginally, wakes up at night: χ22 = 4.95, p = .08). Similarly, sleep latency was prolonged in children with ASD (ASD mean = 26.9 min [SD = 16.1], TD mean = 17.4 min [SD = 10.4], t53 = −2.6, p = .011) (Figure 1C).
      Children with ASD in the fMRI cohort also showed greater sensory sensitivities and more severe auditory processing symptoms as assessed using the Sensory Profile 2 (both p < .001) (Figure 1D; Table S2). Similarly, the correlation between the Sensory Profile 2 Sensory Sensitivity Quadrant score and the CBCL Sleep Problems T score was positive (r = 0.39, p = .057), consistent with the full cohort data (Figure 2B). The correlation between the Sensory Profile 2 Auditory Processing score and the CBCL Sleep Problems T score was not significant (r = 0.23, p = .28, partial correlations controlling for age) (Table S3).

       FC Between the Thalamus and HG Is Increased in Preschoolers With ASD

      FC between the thalamus and HG was significantly increased between the right HG and right and left thalamus in the ASD compared with the TD group (t57 = −2.7, p = .01 and t57 = −2.8, p = .007, respectively) (Figure 3A). FC was also higher between the left HG and left and right thalamus in the ASD group, but the group difference was not significant (t57 = −1.3, p = .19 and t57 = −1.76, p = .08, respectively). Ipsilateral and contralateral FC between HG and the thalamus were significantly elevated in the ASD group (t57 = −2.5, p = .015 and t57 = −2.32, p = .024, respectively). In many TD children, FC estimates were close to zero or negative, in line with previous reports of decreasing thalamocortical FC with anticorrelations frequently observed during deep sleep (
      • Picchioni D.
      • Pixa M.L.
      • Fukunaga M.
      • Carr W.S.
      • Horovitz S.G.
      • Braun A.R.
      • Duyn J.H.
      Decreased connectivity between the thalamus and the neocortex during human nonrapid eye movement sleep.
      ). These results remained similar when global signal regression was included during fMRI data denoising, with FC between the left HG and left and right thalamus additionally showing significant group differences after global signal regression (Figure S1). There was no significant correlation between FC strength and age in the ASD or TD group or when carrying out correlations across the combined cohort for any of the auditory-thalamic FC estimates (all r < 0.2, p > .2). Sleep latency correlated positively with contralateral HG-thalamus FC (partial correlation controlling for root-mean-square deviation and age, r = 0.49, p = .016; ipsilateral FC: r = 0.37, p = .08) (Figure 2C). Results remained largely unchanged when controlling for gestational age at birth, socioeconomic status, or sex in models testing for group differences in FC or associations between FC and sleep latency (see the Supplement).
      Figure thumbnail gr3
      Figure 3Thalamic (Thal)–Heschl’s gyrus (HG) overconnectivity in young children with autism spectrum disorder (ASD). (A) Toddlers and preschoolers with ASD show overconnectivity between the thalamus and HG during natural sleep functional magnetic resonance imaging compared with age-, motion-, and sex-matched typically developing (TD) children (∗ in the ASD matrix mark region of interest [ROI] pairs with significant group differences, p < .05, Benjamini-Hochberg false discovery rate (FDR)–adjusted for multiple comparisons). Functional connectivity was significantly different from zero for HG.R-Thal.L (one-sample t test: t28 = 2.3, p = .03) in the ASD group (but t values for all ROI pairs were positive) and for HG.R-Thal.R (t29 = −2.4, p = .03) in the TD group (with t values for all ROI pairs negative). (B) Ipsilateral and contralateral functional connectivity was similarly elevated in toddlers and preschoolers with ASD and used for behavioral correlations in subsequent analyses. (C) Seed-to-ROI analyses show that thalamocortical overconnectivity is most pronounced for auditory regions. Left (L) and right (R) thalami were used as seeds and all cortical Harvard-Oxford atlas ROIs as targets. t values for the ASD > TD comparison are shown with ∗ marking significant (p < .05, uncorrected) group differences in functional connectivity. (D) Thal-HG overconnectivity in the ASD group appears to be driven by the posterior section of the thalamus. Left and right HG were used as seeds and all voxels in the thalamus as targets.
      To assess whether overconnectivity was specific to auditory regions, seed-to-ROI analysis was carried out using the left and right thalamus as a seed and all cortical Harvard-Oxford ROIs as targets. Based on the pattern of group differences in FC (Figure 3B), results suggest that overconnectivity between the thalamus and cortex is most pronounced in the temporal lobe around the primary auditory cortex, with the right HG having the highest effect size among all comparisons for the left thalamus seed (Cohen’s d = 0.73). Left HG similarly was among the highest effect sizes for both left and right thalamus seeds (Cohen’s d = 0.46 and d = 0.34, respectively).
      Finally, we assessed whether overconnectivity between thalamus and primary auditory cortex was driven by specific thalamic subregions. Results are shown in Figure 3C, with the pattern of FC suggesting that overconnectivity is strongest for the posterior regions of the thalamus, potentially overlapping with the medial geniculate nucleus, and corresponding to the functional parcellations of the thalamus derived from functional thalamocortical connectivity patterns previously, including in infants (
      • Toulmin H.
      • Beckmann C.F.
      • O’Muircheartaigh J.
      • Ball G.
      • Nongena P.
      • Makropoulos A.
      • et al.
      Specialization and integration of functional thalamocortical connectivity in the human infant.
      ,
      • Kumar V.J.
      • van Oort E.
      • Scheffler K.
      • Beckmann C.F.
      • Grodd W.
      Functional anatomy of the human thalamus at rest.
      ,
      • Hwang K.
      • Bertolero M.A.
      • Liu W.B.
      • D’Esposito M.
      The human thalamus is an integrative hub for functional brain networks.
      ).

       BOLD Signal Amplitude Is Increased in the Auditory Cortex During Natural Sleep in Toddlers and Preschoolers With ASD

      ALFF (t57 = 2.4, p = .02) and fALFF (t57 = 1.9, p = .06) were higher in HG in the ASD compared with the TD group (Figure S3), but no significant differences were observed for thalamus ALFF (t57 = −0.65, p = .52) or fALFF (t57 = −0.52, p = .61). fALFF in the thalamus and HG were negatively correlated (F1,55 = 5.1, p < .05) (Figure S4A). In addition, HG-thalamus FC was negatively associated with thalamus fALFF (F1,55 = 10.5, p < .005) and ALFF (F1,55 = 17.2, p < .001) (Figure S4B). There were no significant relationships between HG (f)ALFF and FC. Neither ALFF nor fALFF correlated significantly with Sensory Profile Sensory Sensitivity or Auditory Processing scores or with the CBCL Sleep Problems T score in children with ASD.

      Discussion

      We investigated the relationship between auditory-thalamocortical FC, sleep problems, and sensory sensitivities in toddlers and preschoolers with ASD. Elevated sleep problems and sensory sensitivities, as reported by caregivers, were positively correlated in children with ASD, and increased sleep latency was associated with higher thalamocortical connectivity during natural sleep. Our findings support a model of both atypical sensory processing and sleep problems being linked to early neurodevelopmental disturbances of thalamocortical connectivity.

       Atypical Thalamocortical FC May Underlie Both Sensory Sensitivities and Sleep Problems in Young Children With ASD

      Sensory overresponsivity can interfere with sleep (
      • Cortesi F.
      • Giannotti F.
      • Ivanenko A.
      • Johnson K.
      Sleep in children with autistic spectrum disorder.
      ,
      • Reynolds A.M.
      • Malow B.A.
      Sleep and autism spectrum disorders.
      ), potentially accounting for correlations reported between sleep problems and sensory sensitivities in ASD (
      • Tzischinsky O.
      • Meiri G.
      • Manelis L.
      • Bar-Sinai A.
      • Flusser H.
      • Michaelovski A.
      • et al.
      Sleep disturbances are associated with specific sensory sensitivities in children with autism.
      ). The mechanisms behind sensory sensitivities and sleep problems, however, remain unclear. In this study, we focused specifically on investigating FC between the thalamus and auditory cortex and its association with sleep problems and sensory sensitivities for a number of reasons: 1) connections between the thalamus and auditory cortex are established early in development (
      • Alcauter S.
      • Lin W.
      • Smith J.K.
      • Short S.J.
      • Goldman B.D.
      • Reznick J.S.
      • et al.
      Development of thalamocortical connectivity during infancy and its cognitive correlations.
      ,
      • Ferradal S.L.
      • Gagoski B.
      • Jaimes C.
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      • Vu C.
      • et al.
      System-specific patterns of thalamocortical connectivity in early brain development as revealed by structural and functional MRI.
      ) making them particularly vulnerable to disruption in utero (
      • Barkat T.R.
      • Polley D.B.
      • Hensch T.K.
      A critical period for auditory thalamocortical connectivity.
      ); 2) the formation of tonotopic maps in the auditory cortex is guided by thalamocortical connections in utero, and its disruption can result in atypical sound processing, as has been shown in animal models of ASD (
      • Nagode D.A.
      • Meng X.
      • Winkowski D.E.
      • Smith E.
      • Khan-Tareen H.
      • Kareddy V.
      • et al.
      Abnormal development of the earliest cortical circuits in a mouse model of autism spectrum disorder.
      ,
      • Anomal R.F.
      • de Villers-Sidani E.
      • Brandão J.A.
      • Diniz R.
      • Costa M.R.
      • Romcy-Pereira R.N.
      Impaired processing in the primary auditory cortex of an animal model of autism.
      ); 3) altered sensitivity to and atypical cortical processing of sounds is very common in ASD and has been linked to reduced modulation of thalamocortical FC (
      • Green S.A.
      • Hernandez L.
      • Bookheimer S.Y.
      • Dapretto M.
      Reduced modulation of thalamocortical connectivity during exposure to sensory stimuli in ASD.
      ); and 4) a relationship between atypical sensory gating by the thalamus during sleep, as reflected by increased thalamocortical connectivity and elevated BOLD amplitude, was likely to be most obvious in the auditory cortex given that sleep fMRI is collected in the presence of substantial noise produced by the MRI scanner.
      Similar to findings in older children and adolescents with ASD who underwent resting-state fMRI while awake (
      • Linke A.C.
      • Jao Keehn R.J.
      • Pueschel E.B.
      • Fishman I.
      • Müller R.A.
      Children with ASD show links between aberrant sound processing, social symptoms, and atypical auditory interhemispheric and thalamocortical functional connectivity.
      ,
      • Nair A.
      • Carper R.A.
      • Abbott A.E.
      • Chen C.P.
      • Solders S.
      • Nakutin S.
      • et al.
      Regional specificity of aberrant thalamocortical connectivity in autism.
      ,
      • Woodward N.D.
      • Giraldo-Chica M.
      • Rogers B.
      • Cascio C.J.
      Thalamocortical dysconnectivity in autism spectrum disorder: An analysis of the Autism Brain Imaging Data Exchange.
      ), auditory-thalamic FC was elevated in toddlers and preschoolers with ASD scanned during natural sleep in this study. While auditory-thalamic FC was positively associated with sleep problems, particularly the time it takes children to fall asleep, it did not correlate with sensory sensitivities. The Sensory Profile may not have quantified atypical auditory processing in ASD with high reliability (
      • Schulz S.E.
      • Stevenson R.A.
      Differentiating between sensory sensitivity and sensory reactivity in relation to restricted interests and repetitive behaviours.
      ), because it relies on caregiver report and some questions targeting modality-specific processing also tap into nonsensory aspects of behavior. Previous studies employing sensory stimulation fMRI designs [e.g., (
      • Green S.A.
      • Hernandez L.
      • Bookheimer S.Y.
      • Dapretto M.
      Reduced modulation of thalamocortical connectivity during exposure to sensory stimuli in ASD.
      )] have found thalamocortical overconnectivity in ASD to be associated with sensory overresponsivity [quantified as a composite score derived from the Short Sensory Profile (
      • Dunn W.
      Sensory Profile.
      ) and Sensory Overresponsivity Inventory (
      • Schoen S.A.
      • Miller L.J.
      • Green K.E.
      Pilot study of the sensory over-responsivity scales: Assessment and inventory.
      )]. The lack of a relationship between sensory sensitivities as broadly assessed with the Sensory Profile and auditory-thalamocortical FC is thus likely to reflect the Sensory Profile psychometric limitations.

       Evidence for Early Development of Atypical Auditory-Thalamocortical FC

      Auditory-thalamic FC did not correlate with age, suggesting that the observed overconnectivity reflects early neurodevelopmental disruptions preceding toddler age. Postmortem histology and studies in autism animal models provide evidence for altered establishment of thalamocortical projections and topographical sensory maps in utero, potentially as a result of atypical subplate function (
      • Hoerder-Suabedissen A.
      • Oeschger F.M.
      • Krishnan M.L.
      • Belgard T.G.
      • Wang W.Z.
      • Lee S.
      • et al.
      Expression profiling of mouse subplate reveals a dynamic gene network and disease association with autism and schizophrenia.
      ,
      • Hutsler J.J.
      • Casanova M.F.
      Review: Cortical construction in autism spectrum disorder: Columns, connectivity and the subplate.
      ,
      • Nagode D.A.
      • Meng X.
      • Winkowski D.E.
      • Smith E.
      • Khan-Tareen H.
      • Kareddy V.
      • et al.
      Abnormal development of the earliest cortical circuits in a mouse model of autism spectrum disorder.
      ,
      • Serati M.
      • Delvecchio G.
      • Orsenigo G.
      • Mandolini G.M.
      • Lazzaretti M.
      • Scola E.
      • et al.
      The role of the subplate in schizophrenia and autism: A systematic review.
      ,
      • McFadden K.
      • Minshew N.J.
      Evidence for dysregulation of axonal growth and guidance in the etiology of ASD.
      ,
      • Barkat T.R.
      • Polley D.B.
      • Hensch T.K.
      A critical period for auditory thalamocortical connectivity.
      ,
      • Constantin L.
      • Poulsen R.E.
      • Scholz L.A.
      • Favre-Bulle I.A.
      • Taylor M.A.
      • Sun B.
      • et al.
      Altered brain-wide auditory networks in a zebrafish model of fragile X syndrome.
      ,
      • Kanold P.O.
      • Deng R.
      • Meng X.
      The integrative function of silent synapses on subplate neurons in cortical development and dysfunction.
      ,
      • Molnár Z.
      • Luhmann H.J.
      • Kanold P.O.
      Transient cortical circuits match spontaneous and sensory-driven activity during development.
      ). Recent findings from an infant sibling fMRI study support this interpretation, showing increased FC between the thalamus and somatosensory cortex in 6-week-old infants at high familial risk of ASD, which was associated with increased ASD symptoms at 36 months (
      • Nair A.
      • Jalal R.
      • Liu J.
      • Tsang T.
      • McDonald N.M.
      • Jackson L.
      • et al.
      Altered thalamocortical connectivity in 6-week-old infants at high familial risk for autism spectrum disorder.
      ). Relatedly, in a large group of at-risk infant siblings, Swanson et al. (
      • Wolff J.J.
      • Swanson M.R.
      • Elison J.T.
      • Gerig G.
      • Pruett Jr., J.R.
      • Styner M.A.
      • et al.
      Neural circuitry at age 6 months associated with later repetitive behavior and sensory responsiveness in autism.
      ) reported that thalamus volumes at 12 months differentially predicted language skills at 24 months in those with ASD compared with those with language delay or without familial risk. Abnormalities in auditory cortical processing (
      • Kolesnik A.
      • Begum Ali J.
      • Gliga T.
      • Guiraud J.
      • Charman T.
      • Johnson M.H.
      • et al.
      Increased cortical reactivity to repeated tones at 8 months in infants with later ASD.
      ) and increased prevalence of sleep problems (
      • Humphreys J.S.
      • Gringras P.
      • Blair P.S.
      • Scott N.
      • Henderson J.
      • Fleming P.J.
      • Emond A.M.
      Sleep patterns in children with autistic spectrum disorders: A prospective cohort study.
      ,
      • Nguyen A.K.D.
      • Murphy L.E.
      • Kocak M.
      • Tylavsky F.A.
      • Pagani L.S.
      Prospective associations between infant sleep at 12 months and autism spectrum disorder screening scores at 24 months in a community-based birth cohort.
      ) tied to differences in brain development (
      • MacDuffie K.E.
      • Shen M.D.
      • Dager S.R.
      • Styner M.A.
      • Kim S.H.
      • Paterson S.
      • et al.
      Sleep onset problems and subcortical development in infants later diagnosed with autism spectrum disorder.
      ) have also been observed in infant sibling studies of ASD, further strengthening the notion that underlying neurodevelopmental disruptions occur very early.

       Possible Mechanisms: Atypical Modulation of Thalamocortical FC During Awake and Sleep States and Reduced Sensory Gating

      Unlike in older children and adults scanned awake, average FC between the auditory cortex and the thalamus was close to zero or negative in TD toddlers and preschoolers in our study, with overconnectivity in the ASD group driven by a positive shift in correlation magnitudes. FC between the cortex and subcortical structures changes substantially during sleep, with a reduction in thalamocortical connectivity observed in fMRI studies conducted during deep sleep in adults (
      • Picchioni D.
      • Pixa M.L.
      • Fukunaga M.
      • Carr W.S.
      • Horovitz S.G.
      • Braun A.R.
      • Duyn J.H.
      Decreased connectivity between the thalamus and the neocortex during human nonrapid eye movement sleep.
      ). Mitra et al. (
      • Mitra A.
      • Snyder A.Z.
      • Tagliazucchi E.
      • Laufs H.
      • Elison J.
      • Emerson R.W.
      • et al.
      Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness.
      ) scanned young children (from 6 to 24 months) asleep and compared the lag pattern of FC with that of adults scanned awake and asleep. During N3 sleep, the BOLD responses of the thalamus and cortex showed increased lag compared with wakefulness, in both sleeping 2-year-olds and sleeping adults. While the authors did not report zero-lag FC of the thalamus, increased lag of thalamic BOLD time series during N3 sleep is likely to result in negative or reduced thalamocortical FC compared with wakefulness. Increased FC in the ASD group observed in this study may therefore reflect a lack of thalamocortical modulation during sleep. It is not possible to discern from this study whether those children with increased thalamocortical connectivity during deep sleep would also show heightened thalamocortical connectivity while awake. However, findings from previous studies show that thalamocortical overconnectivity in ASD is also present in the awake state (
      • Linke A.C.
      • Jao Keehn R.J.
      • Pueschel E.B.
      • Fishman I.
      • Müller R.A.
      Children with ASD show links between aberrant sound processing, social symptoms, and atypical auditory interhemispheric and thalamocortical functional connectivity.
      ,
      • Nair A.
      • Carper R.A.
      • Abbott A.E.
      • Chen C.P.
      • Solders S.
      • Nakutin S.
      • et al.
      Regional specificity of aberrant thalamocortical connectivity in autism.
      ,
      • Woodward N.D.
      • Giraldo-Chica M.
      • Rogers B.
      • Cascio C.J.
      Thalamocortical dysconnectivity in autism spectrum disorder: An analysis of the Autism Brain Imaging Data Exchange.
      ). Similarly, in schizophrenia, thalamocortical overconnectivity during the awake state has been observed in multiple studies (
      • Woodward N.D.
      • Karbasforoushan H.
      • Heckers S.
      Thalamocortical dysconnectivity in schizophrenia.
      ,
      • Skåtun K.C.
      • Kaufmann T.
      • Brandt C.L.
      • Doan N.T.
      • Alnæs D.
      • Tønnesen S.
      • et al.
      Thalamo-cortical functional connectivity in schizophrenia and bipolar disorder.
      ,
      • Chen P.
      • Ye E.
      • Jin X.
      • Zhu Y.
      • Wang L.
      Association between thalamocortical functional connectivity abnormalities and cognitive deficits in schizophrenia.
      ) and is associated with reduced sleep spindle density (
      • Baran B.
      • Karahanoğlu F.I.
      • Mylonas D.
      • Demanuele C.
      • Vangel M.
      • Stickgold R.
      • et al.
      Increased thalamocortical connectivity in schizophrenia correlates with sleep spindle deficits: Evidence for a common pathophysiology.
      ), which has also been observed in 2- to 6-year-old children with ASD (
      • Farmer C.A.
      • Chilakamarri P.
      • Thurm A.E.
      • Swedo S.E.
      • Holmes G.L.
      • Buckley A.W.
      Spindle activity in young children with autism, developmental delay, or typical development.
      ). In addition, BOLD signal amplitude in HG was significantly higher during sleep in toddlers and preschoolers with ASD in our study. Across groups, HG BOLD signal amplitude correlated negatively with BOLD signal amplitude in the thalamus, mirroring an observation of increased thalamic but decreased auditory BOLD activity during sleep in healthy young adults (
      • Del Felice A.
      • Formaggio E.
      • Storti S.F.
      • Fiaschi A.
      • Manganotti P.
      The gating role of the thalamus to protect sleep: An f-MRI report.
      ). Increased (f)ALFF in the thalamus was further associated with reduced HG-thalamus FC. Together, these findings suggest heightened ongoing sound processing in the auditory cortex or a lack of habituation to the scanner noise and support an explanation of atypical thalamocortical modulation and reduced sensory gating during sleep in ASD.
      Given the consequences that disrupted sensory processing and sleep might have on development, understanding the relationship between mechanisms underlying sleep problems and the emergence of core ASD symptomatology early in life is of crucial importance. This study is limited by the use of parent-report measures to assess and quantify sensory symptoms and sleep problems. Given the frequency of sleep problems in young children with ASD and the likely influence on neurodevelopment, neuroimaging studies that also collect more direct measures of sleep quality are urgently needed. Ideally, this would include polysomnography and long-term sleep tracking using actigraphy. In addition, despite the modest sample size used for the fMRI analyses, secondary to the practical and methodological challenges associated with obtaining natural sleep MRI data in young children with ASD, our results replicate previously reported associations between sleep problems and sensory sensitivities (
      • Sikora D.M.
      • Johnson K.
      • Clemons T.
      • Katz T.
      The relationship between sleep problems and daytime behavior in children of different ages with autism spectrum disorders.
      ,
      • Mazzone L.
      • Postorino V.
      • Siracusano M.
      • Riccioni A.
      • Curatolo P.
      The relationship between sleep problems, neurobiological alterations, core symptoms of autism spectrum disorder, and psychiatric comorbidities.
      ,
      • Tzischinsky O.
      • Meiri G.
      • Manelis L.
      • Bar-Sinai A.
      • Flusser H.
      • Michaelovski A.
      • et al.
      Sleep disturbances are associated with specific sensory sensitivities in children with autism.
      ), as well as elevated auditory-thalamic FC in older children and adolescents (
      • Linke A.C.
      • Jao Keehn R.J.
      • Pueschel E.B.
      • Fishman I.
      • Müller R.A.
      Children with ASD show links between aberrant sound processing, social symptoms, and atypical auditory interhemispheric and thalamocortical functional connectivity.
      ,
      • Nair A.
      • Carper R.A.
      • Abbott A.E.
      • Chen C.P.
      • Solders S.
      • Nakutin S.
      • et al.
      Regional specificity of aberrant thalamocortical connectivity in autism.
      ,
      • Woodward N.D.
      • Giraldo-Chica M.
      • Rogers B.
      • Cascio C.J.
      Thalamocortical dysconnectivity in autism spectrum disorder: An analysis of the Autism Brain Imaging Data Exchange.
      ). Our findings extend this previous literature and suggest that early developmental abnormalities of thalamocortical connectivity in ASD are linked to both sleep disturbances and sensory problems, laying out a pathway for mechanistic models and ultimately targeted neurobehavioral interventions.

      Acknowledgments and Disclosures

      This work was supported by the National Institutes of Health (Grant No. R01 MH107802 [to IF]).
      ACL, MK, and IF designed the study; ACL, BC, LO, CI, CF, SR, MA, MK, and IF collected the data; ACL, BC, and LO conducted fMRI and behavioral analyses; and ACL, R-AM, and IF drafted the manuscript.
      We thank Lisa Mash, M.S. (San Diego State University), and Tiffany Wang, M.S. (University of California San Diego), for invaluable assistance with data collection. Our strongest gratitude goes to the children and families who so generously dedicated their time and effort to this research.
      A previous version of this article was published as a preprint on bioRxiv: https://www.biorxiv.org/content/10.1101/2021.01.15.426899v1.
      The data that support the findings presented in this manuscript are available in the National Institute of Mental Health Data Archive, a National Institutes of Health–funded data repository (https://nda.nih.gov/). Software used for all analyses are available to researchers for replication.
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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