Abstract
BACKGROUND AND OBJECTIVES:
Agenesis of the corpus callosum (ACC) is a common congenital brain malformation. Early development in ACC remains unexamined, despite the increased likelihood for developmental delays and autistic behaviors. This study compares adaptive functioning in infants/toddlers with isolated ACC to children with other neurodevelopmental conditions and typical development.
METHODS:
Parents of children with ACC completed the Vineland Adaptive Behavior Scale Interview at 6, 12, 18, and 24 months as part of a prospective longitudinal study. Comparison groups included children with fragile X, Down syndrome, high familial likelihood of autism spectrum disorder (both with and without autism spectrum diagnosis), and typical development (total n = 957; total assessments = 2676).
RESULTS:
By 24 months, 29% of children with ACC were delayed in at least 1 domain. Linear mixed effect models showed significant group × time point interactions in all domains. Post-hoc comparisons revealed the ACC group had poorer performance in communication by 6, motor by 12, and daily living by 18 months but equivalent socialization compared with typically developing children; stronger skills across most domains and time points compared with genetic groups; and equivalent communication, stronger socialization, and weaker motor skills compared with the autism group.
CONCLUSIONS:
Although there is significant variability, on average, ACC compromises communication skills by 6 months, with reduced motor and daily living skills by 12 and 18 months, respectively. Multipronged intervention programs are needed for ACC beginning early in the first year of life, possibly leveraging early strengths in social skills.
Agenesis of the corpus callosum (ACC) is a congenital brain malformation defined by partial or complete absence of the brain’s major transverse commissure, the corpus callosum, which is primarily responsible for interhemispheric information transfer.1 ACC is one of the most common congenital brain malformations, present in an estimated 1:4000 live births,2,3 and in 2%–6% of individuals with a developmental disability.4 There is significant heterogeneity in outcomes for individuals with ACC, often related to the presence of additional neuropathology and the presence of co-occurring medical and genetic conditions. However, differences are not generally related to whether the individual has complete or partial ACC.5 Of those with isolated ACC (complete or partial, with no other major neuroanatomical findings), a recent meta-analysis estimated that 71% of people with partial ACC and 76% with of individuals with complete ACC have broadly typical development with subtle learning or cognitive differences observed later in adulthood. The same meta-analysis showed 15% and 16% borderline developmental functioning and 13% and 8% with severe delays (as defined by the individual studies) in partial and complete ACC, respectively.5–7 However, all studies included in the meta-analysis were retrospective, most did not use standardized neurodevelopmental measures, and the age of behavioral evaluation varied across studies. Evidence from controlled neuropsychological studies of individuals with isolated ACC indicates that even in those with broadly typical development, callosal agenesis results in a core constellation of cognitive symptoms that includes reduced interhemispheric transfer, slowed processing, and dificulty with complex novel problem solving.8 Additionally, studies document increased autistic behaviors in older children and adolescents with ACC and an elevated likelihood of autism spectrum disorder (ASD) in adulthood.9,10
Modern ultrasound technology facilitates diagnosis of ACC in utero starting at the 20-week anatomy scan, which is typically corroborated with a fetal or neonatal magnetic resonance imaging. Prenatal diagnosis offers a unique opportunity to study development from birth. However, few studies to date have examined early development in this population using validated measures, leaving providers with insuficient information for families regarding early development and appropriate therapies. Adaptive functioning, the skills required to meet the demands of daily life, are of particular interest and have not been examined. Adaptive functioning skills represent an individual’s ability to independently complete specific tasks required by their environment and change over the lifetime. These skills contribute to functional outcomes and are related to long term quality of life.11–14
Down syndrome (DS) and fragile X syndrome (FX) are two of the most common genetic causes of intellectual disability, which is in part defined by below average adaptive skills.15–17 Similar to ACC, they are also conditions that can be identified prenatally (although FX is most often diagnosed in early childhood) and have a higher incidence of ASD.18,19 Across the lifespan, individuals with DS and FX show global adaptive functioning delays in comparison with typically developing peers, but with some phenotypic specificity.20–24 To our knowledge, only 2 peer-reviewed and published studies have examined DS and/or FX adaptive skills in infancy, with results suggesting that adaptive functioning delays in these populations are evident by 9 months of age,25,26 with distinct trajectories and areas of relative strength.
Early adaptive functioning has also been longitudinally examined in infant siblings of autistic children who are at an increased likelihood for developing ASD compared with the general population.27 Although there is significant heterogeneity in infant siblings of autistic children, by 12-month of age infant siblings who later received a diagnosis themselves (HL+ASD) had significantly lower adaptive skill scores across communication, socialization, and daily living skill (DLS) subdomains than infant siblings who did not meet diagnostic criteria for ASD (HL−) and typically developing infants who were at low likelihood (LL) for developing ASD.28 But no consistent differences have been observed at 6 months in HL+ASD samples. Among a sample of HL and LL infants, Sacrey and colleagues29 identified 3 distinct trajectories of change in Vineland Adaptive Behavior Scale (VABS) scores between 12 and 36 months of age, of which a significantly larger proportion of children with HL+ASD (57%) were assigned to a low and declining trajectory group. In another infant sibling sample, Bussu and colleagues30 also found that young children with decreasing adaptive functioning trajectories were more likely to have ASD.
At this time, it is unclear whether children with ACC present with early adaptive functioning delays across the first 2 years of life, as seen in genetically defined samples (by 6–12 months) or ASD samples (by 12 months), or if alternatively, dificulties emerge later in individuals with ACC as functional tasks become more complex. Although potential delays in motor development may be hypothesized based on neuroanatomy and delays in social skills may be hypothesized based on data from adults,9,10 no study to date has systematically examined adaptive functioning in infants with ACC. Cross-syndrome or cross-condition comparisons promise to elucidate shared vs distinct profiles of impairment and thus inform early intervention efforts, and in some cases pathogenesis. This is the first study to prospectively examine the early development of adaptive functioning in children with ACC.
METHODS
Participants
This longitudinal observational study included data from a convenience sample of 196 infants with ACC (<15 months at enrollment; mean age of enrollment = 5.65 months, SD = 3.28) recruited through social media and family support organizations. Only infants whose caregiver completed at least 1 VABS were included (n = 135). Families were compensated $20 for each completed survey battery and interview. ACC was determined based on detailed parent report (parents referenced medical records to complete the report), and when possible, diagnosis was confirmed via a review of medical records, brain scans, and radiological reports (n = 84 of 135). Study exclusionary criteria included certain co-occurring neurological conditions, specifically neural malformations including polymicrogyria, microcephaly, holoprosencephaly, tumors, and cerebrovascular incidents, in addition to very low birth weight (<1500 g), very preterm birth (<32 weeks), trisomy chromosomal diagnoses, and/ or intractable epilepsy (n = 10; condition revealed after initial enrollment). For the current project, only participants with isolated ACC were used and thus 15 infants were excluded from this analysis due to nonexclusionary, neuroanatomical findings (eg, septo-optic dysplasia, Chiari I malformation, heterotopia), other known genetic syndromes impacting neurodevelopment (Koolen-de Vries Syndrome, Fox-G1 Syndrome), or neurodegenerative conditions (AP4 Hereditary Spastic Paraplegia, Steele-Richardson-Olszewski syndrome). Seven infants were excluded due to corpus callosal hypoplasia diagnosis, and 9 infants were not included in the final sample due to limited information on their callosal malformation. The final sample included 94 infants who met criteria for isolated ACC (80 complete, 14 partial). Approximately 97% of the final sample was retained (3 with-drew, 1 inactive). Ten participants (11%) had missing time points after enrollment, and 13 had not aged into their next time point at the time of analysis (for time points completed by each participant see Supplemental Figure 1). There were no differences in sex, maternal education, or initial income between ACC participants who completed all time points after enrollment and those with missing time points. ACC is a heterogeneous group; thus, no participants were excluded as outliers based on measurement scores. All parents of participants provided informed consent and completed questionnaires in English.
The ACC sample is compared with 5 additional groups (LL, HL−, HL+ASD, FX, and DS). The LL, HL−, and HL +ASD comparison samples were collected exclusively through the Infant Brain Imaging Study (IBIS) network, a prospective longitudinal study of infants at high likelihood of ASD.28 Participants with high familial likelihood for ASD due to having an older sibling with ASD were included in the ASD group if they were diagnosed with ASD themselves at 24 and/or 36 months (HL+ASD, n = 96) and were included in the High Likelihood Negative group if they were not diagnosed with ASD at 24 or 36 months (HL−, n = 359). Additionally, IBIS recruited children with a typically developing older sibling for the Low Likelihood group (LL, n = 211 following exclusion of four children who were later diagnosed with ASD), as well as samples of children with an established diagnosis of DS or FX.31 See Supplemental Information for IBIS recruitment and exclusion criteria. The DS and FX samples from IBIS were supplemented with participants recruited as part of a larger study on early cognitive development at the University of South Carolina (total DS n = 122; total FX n = 75).20 More information on these samples can be found in the Supplemental Information including Supplemental Table 1. Demographic information for the entire sample is presented in Table 1 (additionally see Supplemental Table 2 for age at each time point).
TABLE 1.
Demographic Information
| Demographic | ACC n = 94 | FX n = 75 | DS n = 122 | HL+ASD n = 96 | HL− n = 359 | LL n = 211 | P value |
|---|---|---|---|---|---|---|---|
| Sex, n (%) | .002 | ||||||
| Female | 38 (40) | 21 (28) | 54 (44) | 23 (24) | 161 (45) | 89 (42) | |
| Male | 56 (60) | 54 (72) | 68 (56) | 73 (76) | 198 (55) | 122 (58) | |
| Race, n (%) | |||||||
| American Indian/Native Alaskan | 1 (1) | 1 (1) | — | — | 1 (<1) | — | |
| Asian | 4 (4) | — | 3 (3) | 1 (1) | 5 (1) | 2 (1) | |
| Black | — | 3 (4) | 7 (6) | 3 (3) | 11 (3) | 15 (7) | |
| More than one | 11 (12) | 14 (19) | 10 (8) | 13 (14) | 40 (11) | 19 (9) | |
| White | 74 (79) | 53 (71) | 67 (55) | 78 (81) | 300 (84) | 170 (81) | |
| Other | 3 (3) | — | — | — | — | — | |
| Not answered | 1 (1) | 3 (4) | 31 (25) | 1 (1) | 3 (1) | 4 (2) | |
| Ethnicity, n (%) | |||||||
| Hispanic | 9 (10) | 4 (5) | 13 (11) | 4 (4) | 27 (8) | 11 (5) | |
| Non-Hispanic | 84 (89) | 66 (88) | 74 (61) | 90 (94) | 325 (91) | 194 (92) | |
| Not answered | 1 (1) | 3 (5) | 35 (29) | 2 (2) | 7 (2) | 6 (3) | |
| Maternal age at birth | n = 88 | n = 71 | n = 91 | n = 92 | n = 330 | n = 166 | <.001 |
| Mean (SD) | 32.32 (4.09) | 31.66 (6.24) | 35.21 (5.14) | 33.44 (4.07) | 33.28 (4.42) | 32.86 (4.26) | |
| Paternal age at birth | n = 84 | n = 69 | n = 91 | n = 93 | n = 327 | n = 163 | <.001 |
| Mean (SD) | 34.66 (4.75) | 27.64 (7.51) | 30.39 (7.54) | 36.98 (5.87) | 35.69 (5.06) | 34.17 (5.03) | |
| Household income, n (%) | |||||||
| $34 999 | 7 (7) | 17 (22) | 6 (4) | 13 (13) | 33 (10) | 20 (9) | |
| $35 000–$49 999 | 3 (3) | 5 (6) | 8 (7) | 9 (9) | 35 (10) | 15 (7) | |
| $50 000–$74 999 | 6 (6) | 15 (19) | 15 (12) | 19 (20) | 65 (18) | 40 (19) | |
| $75 000–$99 999 | 18 (19) | 4 (5) | 16 (13) | 14 (15) | 58 (16) | 27 (13) | |
| $100 000–$149 999 | 18 (19) | 11 (14) | 18 (15) | 21 (22) | 65 (18) | 43 (20) | |
| $150 000–$199 999 | 12 (13) | 6 (8) | 7 (6) | 7 (7) | 30 (8) | 8 (4) | |
| >$200 000 | 8 (9) | 3 (4) | 15 (12) | 5 (5) | 32 (9) | 9 (4) | |
| Not answered | 22 (23) | 11 (14) | 39 (32) | 8 (8) | 46 (13) | 51 (24) |
Abbreviations: ACC, Agenesis of the Corpus Callosum; ASD, autism spectrum disorder; DS, Down syndrome; FX, fragile X syndrome; HL+ASD, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age.
Measures and Procedures
We used a prospective, longitudinal design to examine adaptive functioning from 6 to 24 months of age in children with isolated ACC and compared their profiles with those with genetically defined (FX, DS) and behaviorally defined (ASD) neurodevelopmental conditions, as well as those with typical development (HL−, LL). Caregivers of all participant groups were contacted to complete the Vineland Adaptive Behavior Scale - Interview Second or Third Edition (VABS-II/VABS-3)32,33 at 6-, 12-, 18-, and 24-month time points (see Table 2 for the number of participants that completed each time point). VABS interviews were completed by trained and reliable study team members over Zoom when available. If the family could not access Zoom, interviews were completed by phone. The IBIS DS group did not complete interviews at 18 months. Because of minimal data at the 18-month time point in the DS (n = 4) and FX (n = 19) groups, that time point was not included in future analyses for those groups.
TABLE 2.
Number of VABS Completed at Each Timepoint by Group
| ACC | FX | DS | HL+ASD | HL− | LL | |
|---|---|---|---|---|---|---|
| 6 mos | 59 (63) | 31 (41) | 76 (62) | 75 (78) | 287 (80) | 194 (92) |
| 12 mos | 83 (88) | 51 (68) | 94 (77) | 84 (88) | 307 (86) | 162 (77) |
| 18 mos | 68 (72) | — | — | 62 (65) | 207 (58) | 110 (52) |
| 24 mos | 51 (54) | 62 (83) | 74 (60) | 90 (94) | 288 (80) | 161 (76) |
Abbreviations: ACC, Agenesis of the Corpus Callosum; ASD, autism spectrum disorder; DS, Down syndrome; FX, fragile X syndrome; HL+ASD, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age; mos, months. Table presents n and percent of the total group sample at each timepoint.
The VABS is a semistructured caregiver interview assessing adaptive functioning within the domains of communication, daily living, socialization and motor skills, as well as an overall Adaptive Behavior Composite (ABC). The interview was scored and standard scores (SS) for each domain were calculated based on the published normative data (Mean = 100, SD = 15). It should be noted that the ACC group and DS group from IBIS were given the VABS-3, whereas all other participants were given the VABS-II. For more information on these 2 measures and subsequent methodological considerations, please see Supplemental Information. The VABS ABC incorporates the motor domain in the VABS-II, but not in the VABS-3, thus the ABC was not included in these analyses.
Analytic Strategy
Cross-sectional descriptive statistics are presented for each participant group at each time point (Table 3). In the ACC group, VABS scores did not differ by sex at any time point, thus male and female participants were not examined separately across analyses.
TABLE 3.
VABS Domains Standard Score Mean (SD) by Group and Timepoint
| ACC | FX | DS | HL+ASD | HL− | LL | |
|---|---|---|---|---|---|---|
| Communication | ||||||
| 6 mos | 96.64 (15.18) | 85.16 (17.61) | 91.24 (16.40) | 94.19 (15.63) | 96.83 (14.99) | 100.18 (12.51) |
| 12 mos | 93.80 (19.55) | 80.31 (16.22) | 85.71 (17.29) | 90.46 (14.43) | 98.92 (11.42) | 103.13 (9.95) |
| 18 mos | 93.96 (19.69) | — | — | 91.41 (13.51) | 99.07 (9.64) | 104.10 (8.03) |
| 24 mos | 91.10 (19.59) | 78.19 (14.72) | 80.24 (15.19) | 90.29 (12.64) | 101.99 (9.49) | 105.21 (7.50) |
| Daily living skills | ||||||
| 6 mos | 101.34 (14.41) | 92.32 (14.62) | 93.86 (16.58) | 92.20 (12.39) | 95.51 (13.25) | 97.63 (13.52) |
| 12 mos | 98.90 (15.64) | 82.08 (14.73) | 91.12 (16.39) | 90.25 (10.27) | 93.75 (10.40) | 97.40 (10.47) |
| 18 mos | 93.51 (18.09) | — | — | 94.98 (11.86) | 101.89 (9.13) | 105.21 (10.26) |
| 24 mos | 91.29 (15.89) | 79.18 (13.22) | 79.35 (17.07) | 93.47 (10.36) | 102.51 (9.44) | 105.52 (8.42) |
| Socialization skills | ||||||
| 6 mos | 103.68 (14.86) | 88.48 (13.65) | 102.00 (15.00) | 97.35 (11.40) | 100.01 (10.64) | 101.67 (10.40) |
| 12 mos | 102.73 (15.40) | 88.45 (15.85) | 92.69 (13.62) | 93.23 (10.05) | 99.46 (10.64) | 100.00 (10.16) |
| 18 mos | 103.90 (15.54) | — | — | 91.94 (10.37) | 97.33 (8.37) | 100.57 (10.02) |
| 24 mos | 104.75 (13.37) | 81.16 (10.22) | 88.00 (13.83) | 91.06 (10.96) | 100.80 (10.31) | 102.73 (9.63) |
| Motor skills | ||||||
| 6 mos | 93.19 (16.11) | 77.16 (13.77) | 87.83 (18.51) | 89.40 (13.61) | 90.99 (12.76) | 96.08 (12.32) |
| 12 mos | 88.33 (20.20) | 80.90 (18.20) | 92.69 (13.62) | 97.78 (11.53) | 99.86 (10.10) | 102.01 (10.74) |
| 18 mos | 92.49 (21.67) | — | — | 91.94 (10.36) | 99.57 (7.68) | 102.13 (8.09) |
| 24 mos | 89.39 (22.66) | 82.68 (11.29) | 75.00 (21.74) | 96.09 (9.34) | 100.42 (9.71) | 103.03 (8.43) |
Abbreviations: ACC, Agenesis of the Corpus Callosum; ASD, autism spectrum disorder; DS, Down syndrome; FX, fragile X syndrome; HL+ASD, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age; LL, participants at low familial likelihood of developing autism (based on characterization of older sibling and family history), who have not received an autism diagnosis by 36 months of age mos, months.
The data were analyzed using maximum likelihood estimation of linear mixed-effects models in R Version 2023.12.0 + 369 (2023.12.0 + 369) using packages lme4, lmetest, and emmeans. The model included fixed effects of group (ACC, DS, FX, HL+ASD, HL−, LL) and time point (6, 12, 18, 24 months), with time point centered at the mean (15 months), and the group × time point interaction to evaluate longitudinal group differences. The model included a random intercept for each participant to account for the longitudinal repeated measures design (see Supplemental Tables 3–6 for full model). Collection site and sex were initially added to the model as a random and fixed effect, respectively, but did not increase overall Bayesian information criterion, and thus were not included in the final model. The study was underpowered for analysis of time point nested within participants, due to small numbers at specific time points in the genetically defined and ACC groups.
The significance of fixed effects was tested using Type III analysis of variance with Satterthwaite’s method. Estimated marginal means were calculated for each group at each time point (Figures 1, 2, 3 and 4). To further elucidate significant group × time point interactions, planned independent t tests using estimated marginal means (Bonferroni correction within each time point due to multiple comparisons) were completed between the ACC and other study groups across 6-, 12-, 18-, and 24-month time points.
FIGURE 1.
VABS communication: estimated marginal means and 95% confidence intervals by group and time point. *Significant difference (P < .05) between identified group and ACC group. Estimated marginal means based on VABS standard scores with mean = 100 and SD = 15. Abbreviations: ACC, Agenesis of the Corpus Callosum; DS, Down syndrome; FX, fragile X syndrome; HL+, participants at high familial likelihood for ASD (based on verification of an autism spectrum disorder diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age; LL, participants at low familial likelihood of developing autism (based on characterization of older sibling and family history), who have not received an autism diagnosis by 36 months of age; VABS, Vineland Adaptive Behavior Scale.
FIGURE 2.
VABS daily living skills: estimated marginal means and 95% confidence intervals by group and time point. *Significant difference (P < .05) between identified group and ACC group. Estimated marginal means based on VABS standard scores with mean = 100 and SD = 15. Abbreviations: ACC, Agenesis of the Corpus Callosum; DS, Down syndrome; FX, fragile X syndrome; HL+, participants at high familial likelihood for ASD (based on verification of an autism spectrum disorder diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age; LL, participants at low familial likelihood of developing autism (based on characterization of older sibling and family history), who have not received an autism diagnosis by 36 months of age; VABS, Vineland Adaptive Behavior Scale.
FIGURE 3.
VABS socialization: estimated marginal means and 95% confidence intervals by group and time point. *Significant difference (P < .05) between identified group and ACC group. Estimated marginal means based on VABS standard scores with mean = 100 and SD = 15. Abbreviations: ACC, Agenesis of the Corpus Callosum; DS, Down syndrome; FX, fragile X syndrome; HL+, participants at high familial likelihood for ASD (based on verification of an autism spectrum disorder diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age; LL, participants at low familial likelihood of developing autism (based on characterization of older sibling and family history), who have not received an autism diagnosis by 36 months of age; VABS, Vineland Adaptive Behavior Scale.
FIGURE 4.
VABS motor: estimated marginal means and 95% confidence intervals by group and time point. *Significant difference (P < .05) between identified group and ACC group. Estimated marginal means based on VABS standard scores with mean = 100 and SD = 15. Abbreviations: ACC, Agenesis of the Corpus Callosum; DS, Down syndrome; FX, fragile X syndrome; HL+, participants at high familial likelihood for ASD (based on verification of an autism spectrum disorder diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age; LL, participants at low familial likelihood of developing autism (based on characterization of older sibling and family history), who have not received an autism diagnosis by 36 months of age; VABS, Vineland Adaptive Behavior Scale.
RESULTS
ACC Characterization
For all groups, mean and distribution at each time point are presented in Table 3 (original scores) and Supplemental Figures 2, 3, 4, and 5. Table 4 presents percentage of ACC participants with “significant delays” defined as scores 1.5 SD or more below the normative mean (SS≤78, typically 7%) in comparison with the normative population.
TABLE 4.
Number of ACC Participants with a Score 1.5 SDs Below the Normative Mean (Standard Score ≤ 78)
| Vineland Domains | 6 mos | 12 mos | 18 mos | 24 mos |
|---|---|---|---|---|
| Communication | 6 (10%) | 14 (17%)a | 12 (18%)a | 10 (19%)a |
| Daily living skills | 1 (2%) | 9 (11%) | 12 (18%)a | 7 (14%) |
| Socialization | 3 (5%) | 6 (7%) | 5 (7%) | 2 (4%) |
| Motor | 8 (14%) | 19 (23%)b | 11 (16%)a | 12 (24%)b |
Abbreviations: ACC, Agenesis of the Corpus Callosum; mos, months.
Percent represents the percent of the sample at each timepoint. Fischer’s exact one-tailed comparison with expected frequency based on normative population.
P < .05.
P < .01.
Group Comparison
See Supplemental Information for group comparison plots within each VABS domain. Table 5 shows the effect sizes from planned post-hoc comparisons following significant interaction omnibus results.
TABLE 5.
Effect Sizes (Cohen’s d) With Confidence Intervals From Planned Post-Hoc Independent Sample t Tests Using the ACC Group as the Reference
| 6 mos | 12 mos | 18 mos | 24 mos | |
|---|---|---|---|---|
| Communication | ||||
| FX | 1.04a (0.48 to 1.60) | 1.39b (0.92 to 1.86) | — | 1.15b (0.66 to 1.64) |
| DS | 0.55 (0.11 to 0.99) |
0.79b (0.39 to 1.19) | — | 0.85a (0.39 to 1.32) |
| HL+ASD | 0.09 (−0.36 to 0.54) |
0.32 (−0.09 to 0.73) |
0.24 (−0.21 to 0.70) |
0.00 (−0.45 to 0.45) |
| HL− | −0.19 (−0.56 to 0.18) |
−0.54a (−0.87 to −0.21) |
−0.54a (−0.90 to −0.18) |
−1.22b (−1.62 to −0.84) |
| LL | −0.53c (−0.92 to −0.14) |
−1.02b (−1.38 to −0.66) |
−1.16b (−1.55 to −0.76) |
−1.62b (−2.03 to −1.21) |
| Daily living skills | ||||
| FX | 0.92a (0.38 to 1.45) | 1.81b (1.37 to 2.26) | — | 1.08b (0.62 to 1.54) |
| DS | 0.85b (0.43 to 1.28) | 0.78b (0.41 to 1.16) | — | 0.95b (0.51 to 1.39) |
| HL+ASD | 0.83b (0.40 to 1.25) | 0.90b (0.51 to 1.29) | −0.14 (−0.57 to 0.29) |
−0.31 (−0.73 to 0.12) |
| HL− | 0.50c (0.15 to 0.84) | 0.54a (0.23 to 1.85) | −0.83b (−1.18 to −0.49) |
−1.23b (−1.60 to −0.87) |
| LL | 0.30 (−0.07 to 0.66) |
0.17 (−0.17 to 0.51) |
−1.20b (−1.58 to −0.82) |
−1.57b (−1.96 to −1.18) |
| Socialization | ||||
| FX | 1.57b (1.07 to 2.06) | 1.48b (1.08 to 1.89) | — | 2.27b (1.84 to 2.70) |
| DS | 0.21 (−0.17 to 0.60) |
1.03b (0.68 to 1.37) | — | 1.52b (1.12 to 1.93) |
| HL+ASD | 0.55c (0.16 to 0.94) | 0.94b (0.59 to 1.30) | 1.17b (0.78 to 1.57) | 1.30b (0.91 to 1.70) |
| HL− | 0.30 (−0.02 to 0.62) |
0.33 (0.04 to 0.61) |
0.67b (0.36 to 0.99) | 0.31 (−0.03 to 0.64) |
| LL | 0.12 (−0.21 to 0.46) |
0.26 (−0.04 to 0.57) |
0.30 (−0.04 to 0.65) |
0.11 (−0.25 to 0.46) |
| Motor | ||||
| FX | 1.46b (0.89 to 2.02) | 0.76a (0.29 to 1.24) | — | 0.50 (0.01 to 0.99) |
| DS | 0.64c (0.19 to 1.09) | 1.08b (0.67 to 1.49) | — | 1.11b (0.65 to 1.58) |
| HL+ASD | 0.24 (−0.21 to 0.70) |
−0.98b (−1.40 to −0.55) |
−0.65c (−1.11 to −0.20) |
−0.83a (−1.29 to −0.37) |
| HL− | 0.06 (−0.31 to 0.43) |
−1.19b (−1.53 to −0.85) |
−0.83b (−1.20 to −0.47) |
−1.26b (−1.65 to −0.87) |
| LL | −0.44 (−0.83 to −0.06) |
−1.41b (−1.78 to −1.05) |
−1.11b (−1.51 to −0.71) |
−1.54b (−1.95 to −1.12) |
Abbreviations: ACC, Agenesis of the Corpus Callosum; ASD, autism spectrum disorder; DS, Down syndrome; FX, fragile X syndrome; HL+ASD, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have also received an ASD diagnosis by 36 months of age; HL−, participants at high familial likelihood for ASD (based on verification of an ASD diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age; LL, participants at low familial likelihood of developing autism (based on characterization of older sibling and family history), who have not received an autism diagnosis by 36 months of age; mos, months.
Cohen’s d reflects t test between comparison group and ACC. Values tested are reported in Supplemental Information. All Cohen’s d’s reflect Bonferroni corrections.
P < .01.
P < .001.
P < .05.
Communication
Results of the linear mixed model predicting VABS communication revealed significant main effects of group (P < .001), time point (P = .03), and group × time point interaction (P < .001), suggesting the change by time is different between groups (Supplemental Table 3).
Planned post-hoc investigation of the group × time point interaction using estimated marginal means showed that the ACC group had significantly lower communication scores than the LL group and higher mean scores than the FX group at all time points assessed. The ACC group had higher mean communication scores than the DS group at 12 and 24 months, and lower mean scores than the HL− group at 12, 18, and 24 months. The ACC group was statistically equivalent to the HL+ASD group across time points.
DLS
VABS DLS scores also differed by group (P < .001), time point (P < .001), and the group × time point interaction (P < .001) (Supplemental Table 4). The ACC group had statistically higher mean DLS scores than the FX and DS groups at all time points. The ACC group had higher mean DLS scores than the HL+ASD and HL− groups at 6 and 12 months, but by 18 months there was no difference between the HL+ASD group, and the HL− group had higher DLS scores. This pattern remained at 24 months. The ACC group was statistically equivalent to the LL group at 6 and 12 months, and lower at 18 and 24 months.
Socialization
VABS socialization scores were significantly different by group (P < .001), time point (P < .001), and group × time point (P < .001) (Supplemental Table 5).
At 6 months, the ACC group scored higher than both the FX and HL+ASD groups. At 12 and 24 months the ACC group’s estimated mean socialization scores were significantly higher than the FX, DS, and HL+ASD groups, and equivalent to the HL− and LL groups.
Motor Skills
VABS motor skill scores significantly differed by group (P < .001), time point (P < .001), and group × time point (P < .001) (Supplemental Table 6).
Although they did not differ at 6 months, motor scores from the ACC group fell significantly below the HL+ASD, HL−, and LL groups at 12, 18, and 24 months.
In contrast, the ACC group had higher motor scores than the DS group at all time points and higher scores than the FX group at 6 and 12 months but did not differ from FX at 24 months.
DISCUSSION
The current study is the first to prospectively examine adaptive functioning in a population with ACC during the infant and toddler years. Although ACC is often identified in utero, little is known about early development, making it challenging for providers to educate families on the course of development and to share empirically informed clinical recommendations regarding early intervention.
Our isolated ACC sample did not exhibit elevated likelihood of significant delay (1.5 SDs below the mean) in any domain at 6 months, nor in the socialization domain at any time point. However, by 24 months, 29% of our sample exhibited significant delays in at least one domain, primarily communication and/or motor skills. This is broadly consistent with a recent meta-analysis of developmental outcomes showing borderline to impaired functioning in 24%−27% of individuals with prenatal diagnoses of isolated ACC.5 In comparison with our typically developing sample (LL), the ACC participants demonstrated a communication score distribution that was lower at 6 months and remained lower across all time points. Otherwise, the ACC group broadly reflected the normative sample at 6 months, but on average showed lower score distributions that emerged at 12 months in motor skills and 18 months in DLS, with significantly lower scores at 24 months across communication, DLS, and motor skills. Socialization skills on the other hand were a relative strength and overlapped with the LL group across time points.
Prenatal and perinatal diagnosis of ACC affords the unique opportunity to initiate targeted supportive interventions in the first year of life, at the onset of skill development. Our findings reveal emergence of mild developmental delays within the first year of life in children with isolated ACC and progression to clinically significant delays by 24 months for a subset. Thus, we support referral to early intervention at birth to assist with closer developmental monitoring and to facilitate development in domains impacted by ACC (specifically communication and motor skills), through enriched practice of foundational skills and expedited implementation of targeted interventions to reduce delays.
In comparison with the genetically defined groups, this neurologically defined group (ACC) showed higher scores across most domains (statistically equivalent to DS group in communication and socialization at 6 months, and FX group in the motor domains at 24 months). Although the current research is focused on ACC, some intriguing findings were revealed among the genetically defined groups. Specifically, the FX and DS groups tended to show early and persistent delays that were evident starting at 6 months (communication and motor). Although previous work has showed significant delays beginning within the first year, this is the first longitudinal study of FX and DS children to show significant differences at 6 months,25,26 underscoring the need for early intervention in these groups (effect sizes from planned post-hoc tests for the FX and DS groups are presented in Supplemental Table 7 and 8 respectively).
The corpus callosum has been implicated in the early ASD phenotype, but more work is needed to elucidate its specific role in communicative competence.34–38 In this study, the ACC group performed comparably to the HL+ASD group in communication and DLS at most time points (equivalent on communication at all ages, DLS at 18 and 24 months). However, the ACC group showed lower motor scores (12, 18, and 24 months) and higher socialization scores (all time points) compared with the HL+ASD group. Although the VABS does not assess the full range of behaviors associated with ASD, social challenges as measured by the VABS were evident by 12 months in the HL+ASD sample. In contrast, social skills represented a relative strength for this sample of infants and toddlers with ACC. Given that social challenges have been observed in individuals with ACC later in life, it is possible these dificulties emerge over time as environmental and interpersonal demands increase for individuals with ACC. It is also possible that early indicators are not fully captured by the VABS. The proportion of toddlers with ACC who meet ASD diagnostic criteria is currently unknown, with limited evidence from adults suggesting approximately 15% meet criteria in early childhood.9 Future work should specifically measure autistic behaviors to delineate the developmental course of children with ACC who go on to receive an ASD diagnosis.
Limitations
The current study had limitations that warrant acknowledgment. Foremost, in a subset of ACC participants, investigators were unable to directly review medical records to confirm the neurological diagnoses provided by parents, allowing for the possibility of misclassification or missing information around exclusionary criteria. Additionally, consideration of ASD diagnoses within the ACC, FX, or DS groups was beyond the scope of this project but is important to examine in future work. Although thought to be minimal, the use of both VABS-II and VABS-3 introduces a potential confound to between-group comparisons. Indeed, we cannot rule out the confound of version differences across groups (see discussion in Supplemental Information). Prior work suggests VABS-3 may produce slightly lower scores, with larger differences at lower levels of adaptive behavior.39 In our age range, the largest differences were in the socialization domain.33 Thus, VABS-3 scores may have artificially minimized the elevation of socialization in ACC relative to the other groups, and we cannot eliminate the possibility that VABS-3 scores in other domains may have artificially inflated significant differences from typically developing children and artificially minimized differences from the other groups. Finally, participants in the ACC group were recruited through parent support groups and social media, which reaches only a subset of all families that have an infant with ACC. The racial and ethnic and sociodemographic makeup of our sample is not likely representative of the population, thus more diverse samples will improve generalizability of future studies.
CONCLUSIONS
This study describes early development in children with ACC, and highlights areas for early intervention. Importantly, the majority of infants and toddlers with isolated ACC demonstrated broadly intact parent-reported adaptive skills. However, on average and in comparison with typically developing peers, there is evidence of mild early delays in communication and motor skills observable at 6 and 12 months, respectively, with a mild delay in DLS by 18 months. Children with ACC show higher adaptive skills than those with common genetic conditions, and similar skills to those with HL+ASD. However, unlike HL+ASD, children with ACC show a relative early strength in their social skills. Given the prior evidence that adolescents and adults with ACC often have challenges with aspects of social functioning, it will be important to further elucidate this finding and continue to examine how social skills change over time. The current study highlights the need for multi-pronged intervention programs to address ACC, beginning early in the first year of life and specifically emphasizing communication and motor development. Thus, we recommend a referral to early intervention at birth. Approximately 97% continue to track adaptive functioning beyond the first few years of life and inform possible predictors of adaptive functioning delays. Additionally including children with other forms of corpus callosum malformation and co-occurring conditions may facilitate more refined prediction of early developmental delays in callosal dysgenesis.
Supplementary Material
WHAT’S KNOWN ON THIS SUBJECT:
Agenesis of the corpus callosum (ACC) can often be diagnosed in utero at the 20-week anatomy ultrasound scan. Developmental outcomes for individuals with isolated ACC are highly heterogeneous, with the majority showing broadly intact cognitive development and around 24% showing borderline to severe cognitive challenges.
WHAT THIS STUDY ADDS:
Little is known about early development in children with ACC. This study characterizes adaptive functioning in infants and toddlers with isolated ACC and compares their development with typically developing children and children with genetically or behaviorally defined neurodevelopmental conditions.
FUNDING:
This study was supported by funding from the National Institutes of Health including R01 HD092430 to L. Paul, R01 HD088125 to K. Botteron/Natasha Marrus, R01 HD059084 to H. Hazlett, R01 HD055741 to J. Piven, R01 MH90194 and R01 MH107573 to J. Roberts, and F32 HD097877 to E. Will. The National Institutes of Health had no role in the design and conduct of the study or in the interpretation or presentation of the results herein.
Author Contributions:
Dr Haisley conceptualized the specific research question addressed in the paper, coordinated and collected data, supervised data collection, carried out initial analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript. Ms Hantzsch, Ms Turner, Drs Swanson, Wolff, Burrows, Botteron, Dager, Estes, Ms Flake, Drs McKinstry, Pandey, Schultz, Shen, St. John, Zweigenbaum, Hazlett, Marrus, Will, and Roberts coordinated and supervised data collection and critically reviewed and revised the manuscript. Mr Glick, Drs Sung, and Elison managed the data, oversaw and contributed to data analysis, and critically reviewed and revised the manuscript. Dr Piven designed the Infant Brain Imaging Study, which informed the design of the cohorts with agenesis of the corpus callosum (ACC), fragile X syndrome, and Down syndrome. Drs Paul and Elison conceptualized and designed the study, procured funding for the ACC sample collection, supervised data collection, supervised data analysis, and critically reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
ABBREVIATIONS
- ABC
Adaptive Behavior Composite, composite score from the Vineland Adaptive Behavior Scale
- ACC
agenesis of the corpus callosum
- ASD
autism spectrum disorder
- DS
Down syndrome
- DLS
daily living skills (subscale on the Vineland-II/3)
- FX
fragile X syndrome
- HL+ASD
participants at high familial likelihood for autism spectrum disorder (based on verification of an autism spectrum disorder diagnosis in older sibling), who have also received an autism spectrum disorder diagnosis by 36 months of age
- HL−
participants at high familial likelihood for autism spectrum disorder (based on verification of an autism spectrum disorder diagnosis in older sibling), who have not received an autism diagnosis by 36 months of age
- IBIS
Infant Brain Imaging Study, a prospective longitudinal study of infants at high likelihood of autism
- LL
participants at low familial likelihood of developing autism (based on characterization of older sibling and family history), who have not received an autism diagnosis by 36 months of age
- SS
standard score
- TD
typically developing
- VABS
Vineland Adaptive Behavior Scale
Footnotes
CONFLICT OF INTEREST DISCLOSURES: The authors declare no financial or other potential conflicts of interest.
REFERENCES
- 1.Tanaka-Arakawa MM, Matsui M, Tanaka C, et al. Developmental changes in the corpus callosum from infancy to early adulthood: a structural magnetic resonance imaging study. PLoS One. 2015; 10(3):e0118760. PubMed doi: 10.1371/journal.pone.0118760 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Glass HC, Shaw GM, Ma C, Sherr EH. Agenesis of the corpus callosum in California 1983–2003: a population-based study. Am J Med Genet A. 2008;146A(19):2495–2500. PubMed doi: 10.1002/ajmg.a.32418 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wang LW, Huang CC, Yeh TF. Major brain lesions detected on sonographic screening of apparently normal term neonates. Neuroradiology. 2004;46(5):368–373. PubMed doi: 10.1007/s00234-003-1160-4 [DOI] [PubMed] [Google Scholar]
- 4.Jeret JS, Serur D, Wisniewski K, Fisch C. Frequency of agenesis of the corpus callosum in the developmentally disabled population as determined by computerized tomography. Pediatr Neurosci. 1985;12(2):101–103. PubMed doi: 10.1159/000120229 [DOI] [PubMed] [Google Scholar]
- 5.D’Antonio F, Pagani G, Familiari A, et al. Outcomes associated with isolated agenesis of the corpus callosum: a meta-analysis. Pediatrics. 2016;138(3):e20160445. PubMed doi: 10.1542/peds.2016-0445 [DOI] [PubMed] [Google Scholar]
- 6.Hart AR, Vasudevan C, Griffiths PD, et al. Antenatal counselling for prospective parents whose fetus has a neurological anomaly: part 2, risks of adverse outcome in common anomalies. Dev Med Child Neurol. 2022;64(1):23–39. PubMed doi: 10.1111/dmcn.15043 [DOI] [PubMed] [Google Scholar]
- 7.Palmer EE, Mowat D. Agenesis of the corpus callosum: a clinical approach to diagnosis. Am J Med Genet C Semin Med Genet. 2014; 166C(2):184–197. PubMed doi: 10.1002/ajmg.c.31405 [DOI] [PubMed] [Google Scholar]
- 8.Brown WS, Paul LK. The neuropsychological syndrome of agenesis of the corpus callosum. J Int Neuropsychol Soc. 2019;25(3):324–330. PubMed doi: 10.1017/S135561771800111X [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Paul LK, Corsello C, Kennedy DP, Adolphs R. Agenesis of the corpus callosum and autism: a comprehensive comparison. Brain. 2014; 137(Pt 6):1813–1829. PubMed doi: 10.1093/brain/awu070 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Lau YC, Hinkley LB, Bukshpun P, et al. Autism traits in individuals with agenesis of the corpus callosum. J Autism Dev Disord. 2013; 43(5):1106–1118. PubMed doi: 10.1007/s10803-012-1653-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chiang HM, Wineman I. Factors associated with quality of life in individuals with autism spectrum disorders: a review of literature. Res Autism Spectr Disord. 2014;8(8):974–986. doi: 10.1016/j.rasd.2014.05.003 [DOI] [Google Scholar]
- 12.Miller LE, Burke JD, Troyb E, Knoch K, Herlihy LE, Fein DA. Preschool predictors of school-age academic achievement in autism spectrum disorder. Clin Neuropsychol. 2017;31(2):382–403. PubMed doi: 10.1080/13854046.2016.1225665 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Tassé MJ, Luckasson R, Schalock RL. The relation between intellectual functioning and adaptive behavior in the diagnosis of intellectual disability. Intellect Dev Disabil. 2016;54(6):381–390. PubMed doi: 10.1352/1934-9556-54.6.381 [DOI] [PubMed] [Google Scholar]
- 14.Woolf S, Woolf CM, Oakland T. Adaptive behavior among adults with intellectual disabilities and its relationship to community independence. Intellect Dev Disabil. 2010;48(3):209–215. PubMed doi: 10.1352/1944-7558-48.3.209 [DOI] [PubMed] [Google Scholar]
- 15.American Psychiatric Association. Diagnostic and Statistical Manual Of Mental Disorders, Fifth Edition. doi: 10.1176/appi.books.9780890425596 [DOI] [Google Scholar]
- 16.Mai CT, Isenburg JL, Canfield MA, et al. ; National Birth Defects Prevention Network. National population-based estimates for major birth defects, 2010–2014. Birth Defects Res. 2019;111(18): 1420–1435. PubMed doi: 10.1002/bdr2.1589 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Crawford DC, Acuña JM, Sherman SL. FMR1 and the fragile X syndrome: human genome epidemiology review. Genet Med. 2001; 3(5):359–371. PubMed doi: 10.1097/00125817-200109000-00006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kaufmann WE, Kidd SA, Andrews HF, et al. Autism spectrum disorder in fragile X syndrome: cooccurring conditions and current treatment. Pediatrics. 2017;139(suppl 3):S194–S206. PubMed doi: 10.1542/peds.2016-1159F [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.DiGuiseppi C, Hepburn S, Davis JM, et al. Screening for autism spectrum disorders in children with Down syndrome: population prevalence and screening test characteristics. J Dev Behav Pediatr. 2010; 31(3):181–191. PubMed doi: 10.1097/DBP.0b013e3181d5aa6d [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Will EA, Roberts JE. Motor influences on communication: comparisons between Down Syndrome and fragile X syndrome. Am J Intellect Dev Disabil. 2021;126(6):460–476. PubMed doi: 10.1352/1944-7558-126.6.460 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Fidler D, Hepburn S, Rogers S. Early learning and adaptive behaviour in toddlers with Down syndrome: evidence for an emerging behavioural phenotype? Downs Syndr Res Pract. 2006;9(3):37–44. PubMed doi: 10.3104/reports.297 [DOI] [PubMed] [Google Scholar]
- 22.Dykens EM, Hodapp RM, Evans DW. Profiles and development of adaptive behavior in children with Down syndrome. Downs Syndr Res Pract. 2006;9(3):45–50. PubMed doi: 10.3104/reprints.293 [DOI] [PubMed] [Google Scholar]
- 23.Baumgardner TL, Reiss AL, Freund LS, Abrams MT. Specification of the neurobehavioral phenotype in males with fragile X syndrome. Pediatrics. 1995;95(5):744–752. PubMed doi: 10.1542/peds.95.5.744 [DOI] [PubMed] [Google Scholar]
- 24.Klaiman C, Quintin EM, Jo B, et al. Longitudinal profiles of adaptive behavior in fragile X syndrome. Pediatrics. 2014;134(2):315–324. PubMed doi: 10.1542/peds.2013-3990 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Will EA, Caravella KE, Hahn LJ, Fidler DJ, Roberts JE. Adaptive behavior in infants and toddlers with Down syndrome and fragile X syndrome. Am J Med Genet B Neuropsychiatr Genet. 2018;177(3): 358–368. PubMed doi: 10.1002/ajmg.b.32619 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Caravella KE, Roberts JE. Adaptive skill trajectories in infants with fragile X syndrome contrasted to typical controls and infants at high risk for autism. Res Autism Spectr Disord. 2017;40:1–12. PubMed doi: 10.1016/j.rasd.2017.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ozonoff S, Young GS, Bradshaw J, et al. Familial recurrence of autism: updates from the Baby Siblings Research Consortium. Pediatrics. 2024;154(2):e2023065297. PubMed doi: 10.1542/peds.2023-065297 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Estes A, Zwaigenbaum L, Gu H, et al. ; IBIS network. Behavioral, cognitive, and adaptive development in infants with autism spectrum disorder in the first 2 years of life. J Neurodev Disord. 2015;7:1–10. PubMed doi: 10.1186/s11689-015-9117-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sacrey LR, Zwaigenbaum L, Bryson S, et al. Developmental trajectories of adaptive behavior in autism spectrum disorder: a high-risk sibling cohort. J Child Psychol Psychiatry. 2019;60(6): 697–706. PubMed doi: 10.1111/jcpp.12985 [DOI] [PubMed] [Google Scholar]
- 30.Bussu G, Jones EJH, Charman T, Johnson MH, Buitelaar JK; BASIS Team. Latent trajectories of adaptive behaviour in infants at high and low familial risk for autism spectrum disorder. Mol Autism. 2019;10(1):13. PubMed doi: 10.1186/s13229-019-0264-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Swanson MR, Wolff JJ, Shen M-D, et al. ; Infant Brain Imaging Study (IBIS) Network. Development of white matter circuitry in infants with fragile X syndrome. JAMA Psychiatry. 2018;75(5):505–513. PubMed doi: 10.1001/jamapsychiatry.2018.0180 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Sparrow SS, Cicchetti DV, Balla DA. Vineland-II Adaptive Behavior Scales: Survey Forms Manual. AGS Publishing; 2005. [Google Scholar]
- 33.Sparrow SS, Cicchetti DV, Saulnier CA. Vineland Adaptive Behavior Scales, Third Edition (Vineland-3). Pearson; 2016. [Google Scholar]
- 34.Wolff JJ, Gerig G, Lewis JD, et al. ; IBIS Network. Altered corpus callosum morphology associated with autism over the first 2 years of life. Brain. 2015;138(Pt 7):2046–2058. PubMed doi: 10.1093/brain/awv118 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Wolff JJ, Swanson MR, Elison JT, et al. Neural circuitry at age 6 months associated with later repetitive behavior and sensory responsiveness in autism. Mol Autism. 2017;8:8. PubMed doi: 10.1186/s13229-017-0126-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Estrada KA, Govindaraj S, Abdi H, et al. ; IBIS Network. Language exposure during infancy is negatively associated with white matter microstructure in the arcuate fasciculus. Dev Cogn Neurosci. 2023;61:101240. PubMed doi: 10.1016/j.dcn.2023.101240 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.McFayden TC, Rutsohn J, Cetin G, et al. ; IBIS Network. White matter development and language abilities during infancy in autism spectrum disorder. Mol Psychiatry. 2024;29(7):2095–2104. PubMed doi: 10.1038/s41380-024-02470-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Swanson MR, Wolff JJ, Elison JT, et al. Splenium development and early spoken language in human infants. Dev Sci. 2017;20(2): PubMed doi: 10.1111/desc.12360 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Farmer C, Adedipe D, Bal VH, Chlebowski C, Thurm A. Concordance of the Vineland Adaptive Behavior Scales, second and third editions. J Intellect Disabil Res. 2020;64(1):18–26. PubMed doi: 10.1111/jir.12691 [DOI] [PMC free article] [PubMed] [Google Scholar]
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