pmc80069334649J Dev Behav PediatrJ Dev Behav PediatrJournal of developmental and behavioral pediatrics : JDBP0196-206X1536-731235943360956100510.1097/DBP.0000000000001100NIHMS1800597ArticlePilot Study of Maternal Autoantibody Related Autism (MAR ASD)AngkustsiriKathleenMD, MAS12FussellJill J.MD3BennettAmandaMD, MPH4SchauerJosephBS5Ramirez-CelisAlexandraPhD5HansenRobin L.MD12Van de WaterJudyPhD25 Developmental Behavioral Pediatrics, Department of Pediatrics, University of California Davis Health, Sacramento, CA UC Davis MIND Institute, Sacramento, California Developmental-Behavioral Pediatrics and Rehabilitative Medicine, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA. Developmental and Behavioral Pediatrics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA Rheumatology, Allergy, and Clinical Immunology, Department of Internal Medicine, University of California, Davis, CACorresponding Author: Kathleen Angkustsiri, MD, 2825 50th Street, Sacramento, CA, 95817, phone (916) 703-0235, fax (916) 703-0243 kangkustsiri@ucdavis.edu652022Oct-Nov202201620220162023438465471Objective:

To investigate the presence of Maternal Autoantibody-Related Autism Spectrum Disorder (MAR ASD) in 2 geographically distinct DBPNet clinical sites (Pennsylvania and Arkansas). MAR ASD is a biologically defined subtype of autism spectrum disorder that is defined by the presence of autoantibodies specific to proteins in the fetal brain and present in approximately 20% of a Northern California sample but has not been studied in other states.

Methods:

Sixty-eight mothers of children with ASD were recruited from 2 DBPNet clinics and provided blood samples. Mothers also completed behavioral questionnaires about their children, and data from the child’s clinical diagnostic assessment was abstracted.

Results:

Mean age of mothers was 38.5±6.1 years, and mean age of children was 8.3±2.7 years. MAR ASD was present in 24% of the sample and similar across sites. Children of +MAR mothers had more severe autism symptoms as measured by ADOS comparison scores (W=3604, p<0.001) and the Social Communication Questionnaire (W=4556; p<0.001). There were no differences in IQ, adaptive function, or aberrant behavior.

Conclusion:

MAR ASD is a subtype of autism that is present in similar frequencies across 3 states and related to autism severity.

Autismmaternal autoantibodiesanti-fetal brain autoantibodiesimmunepre- and peri-natal risk factors

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects 1 in 44 children in the United States1. The etiology is still unknown; however, there are likely multiple biologic, genetic, and environmental causes that contribute to ASD risk 2–4. There is significant heterogeneity within ASD, and at least one meaningful subgroup involves the maternal gestational immune environment 5–7.

Maternal Autoantibody Related autism spectrum disorder (MAR ASD) is a subtype of ASD that is characterized by maternal reactivity to specific autoantigens present in the developing brain 8–12. Animal studies suggest that the presence of these maternal antibodies produces ASD hallmark behaviors in offspring, such as repetitive behaviors, communication difficulties, and atypical social behaviors 13–15. In humans, maternal autoantibody reactivity against 8 proteins highly expressed in the developing brain have been identified, along with ASD-specific patterns of reactivity (for 2 or more proteins) that are present only in mothers of children with ASD and not in mothers of children with typical development or developmental delay 16,17. These maternal autoantibody patterns may be a biomarker of risk for approximately 20% of children with ASD 16. In addition, reactivity to one of these proteins, collapsin response mediator protein 1 (CRMP1), is associated with more severe Autism Diagnostic Observation Schedule (ADOS) scores16.

ASD prevalence varies greatly across the United States, with a range of 1 in 61 (1.65%) in Missouri to 1 in 25 (3.9%) in California based on records from 11 states1. Our MAR ASD studies have predominantly been conducted with samples from mothers in Northern California. Therefore, we conducted a pilot study utilizing 2 sites from Developmental-Behavioral Pediatrics Research Network (DBPNet) to inform a future larger study involving more sites across the network. We report preliminary data on the frequency of MAR ASD in these two geographically diverse populations and the association of these antibody patterns with child behaviors, including ASD severity.

METHODSParticipants

Participants included biological mothers of children ages 2–12 years diagnosed with ASD through Developmental-Behavioral Pediatrics (DBP) clinics at 2 DBPNet Sites, the Children’s Hospital of Philadelphia (CHOP) and Arkansas Children’s Hospital and Research Institute (ACHRI). DBPNet is a multi-center research network involving DBP programs at 16 academic medical centers.

All procedures were approved by the Institutional Review Board at CHOP through the DBPNet Network Coordinating Center, and all participants provided informed consent prior to inclusion in the study.

Mothers were recruited from the 2 DBP clinics if their child underwent clinical evaluation for ASD, was diagnosed with ASD using DSM criteria, and had Autism Diagnostic Observation Schedule (ADOS)18,19 scores above the ASD cut-off. Five-hundred and twenty-two eligible mothers (CHOP: 314; ACHRI: 208) were contacted via email or (mailed) letters to participate in the study. Of those invited, 97 mothers (CHOP: 51; ACHRI 46) responded, with 68 participants enrolled. Exclusion criteria included the presence of a known genetic disorder or sensory/motor impairments (i.e. visual/hearing deficits, etc.) that precluded standardized assessment.

Study procedures included a research study visit for the collection of a blood sample from the mother and completion of additional questionnaires, including the Social Communication Questionnaire (SCQ)20, the Early Development Questionnaire21, and the study demographic form, which included information about family history. Data was abstracted from chart review of the child’s ASD diagnostic evaluation from DBP clinic, including ADOS scores, IQ/cognitive scores, adaptive functioning scores (Vineland Adaptive Behavior Scales-II) 22, and scores from the Aberrant Behavior Checklist (ABC) 23. The ADOS is a semi-structured standardized diagnostic assessment of ASD symptoms that is available in 5 modules (Module 1, 2, 3, 4, and Toddler) depending on an individual’s developmental level and language abilities. Each module has a separate algorithm that yields a total score that can be classified into one of 3 categories: autism, ASD, or non-spectrum. An ADOS Comparison Score (also known as calibrated severity score) can be calculated on a 1–10 scale and measures autism severity across modules. While all clinic notes documented that an ADOS was performed and above cut-off for ASD, some encounters did not report specific ADOS scores needed to calculate an ADOS comparison score. Due to the variation in clinical assessments across ages and sites, cognitive scores were predominantly obtained from the Stanford Binet-5 24, although other assessments included the Wechsler Intelligence Scale for Children-IV 25, Differential Ability Scales-II 26, and Mullen Scales of Early Learning 27. Cognitive scores were categorized as average or above (>85); Low Average (70–84), or Extremely Low (<69) given the variety of assessments performed.

Sample collection and preparation

Maternal blood was collected in citrate dextrose (BD Diagnostic) and plasma was separated, labeled, aliquoted, and stored at −80 °C. Prior to use, samples were thawed at room temperature (RT), vortexed, and centrifuged at 13,000 RPM for 10 min. This collection protocol was identical to the previous studies16.

Enzyme-linked immunosorbent assay (ELISA).

IgG antibody reactivity of plasma samples against each antigen was determined by ELISA using commercially available proteins, and the assay conditions were optimized for each protein as previously described 28. Briefly, microplates were coated with 100 μl of antigen in carbonate coating buffer pH 9.6, incubated overnight at 4 °C, washed four times with Phosphate Buffered Saline Tween-20 (PBST) 0.05%, and blocked with 2% Super Block (Thermo Scientific, Rockford, lL) for 1 hr at RT. 100μl of diluted sample was added to each well and incubated for 1.5hr at RT, followed by 4 washes with PBST 0.05%. Goat-anti human IgG-HRP (Kirkegaard & Perry Laboratories, Inc., Gaithersburg, MA) was diluted at 1:10,000 in PBST 0.05%, incubated for 1 hr at RT, and washed 4 times. Finally, 100μl of BD optEIA was added and the reaction was stopped with 50 μl of 2N HCl after 4 min. The absorbance was measured at 490–450 nm using an iMark Microplate Absorbance Reader (Biorad, Hercules, CA, USA).

After plate-plate normalization, a positive cut-off was established for each antigen using an ROC (Receiver operating characteristic) curve and Youden’s index as previously described. The positive-control samples used to create the ROC were not included in the analysis. Mothers with MAR positivity are referred to as “+MAR,” while those without the presence of maternal autoantibodies are described as “−MAR.”

Data Analysis

Descriptive statistics are presented for demographic characteristics and MAR positivity (+MAR) prevalence. Non-parametric analyses (Wilcoxon rank-sum) were utilized to compare group differences due to the presence of skewed data. Fisher’s exact test was used to compare categorical data.

RESULTS

Demographic characteristics are presented in Table 1. In total, 68 mothers participated and provided samples for analysis. On average, mothers were 38.5 years old and slightly older at CHOP (40.5 yrs. vs. 36.8 yrs; p=0.014). At diagnosis, children were an average of 8.25 years old, with a range of 2.5 to 14 years, and were similar in age across sites.

MAR ASD prevalence across sites

MAR positivity was similar across sites, with an overall prevalence of 23.5% (16 of 68 samples). At CHOP, 21% (7/33) of mothers demonstrated MAR positivity (+MAR), while 26% (9/35) of ACHRI mothers were +MAR (n.s.). There were no significant differences in mothers’ age based on MAR positivity (Wilcoxon rank sum W=365, p=0.7). Children of +MAR mothers ranged from 3.4–11.25 years of age and did not differ in age from children of −MAR mothers (Wilcoxon rank sum W=495, p=0.3). Ten of the 16 +MAR mothers had other children at the time of blood draw based on the demographic questionnaire; of those, 3 had other children with ASD and 2 more had other children with developmental delay. The oldest child (11.25 years) of a +MAR mother in this sample also had a 9-year-old sibling with a diagnosis of ASD. In −MAR mothers, 37 of 52 had other children at the time of blood draw; five had other children with ASD, while 3 others had other children with developmental delay.

Figure 1 describes the known MAR ASD patterns that are composed of a combination of two or more antigens. Five mothers had positivity for CRMP1+GDA (4 from ACHRI) and 3 for CRMP1+CRMP2 (2 from ACHRI), including one mother that was positive for CRMP1+CRMP2+GDA.

Behavioral Characteristics associated with MAR ASD reactivityAutism symptoms:

While clinical records indicated that all children had total ADOS scores above the ASD cut-off, ADOS Comparison Scores were only available for 53 children (11 children of +MAR mothers; 42 children of −MAR mothers). Seventeen children received Module 1, 20 received Module 2, and 16 received Module 3. ADOS severity was significantly different between the groups (Wilcoxon rank sum W=3604, p<0.001). ADOS comparison scores ranged from 3–10 for children in the −MAR group, while ADOS comparison scores ranged from 6–10 for children in the +MAR group (see Figure 2a).

Total SCQ scores (Figure 2b) were available for 63 children (16 +MAR; 51 −MAR) and significantly higher in children of +MAR mothers than children of −MAR mothers (W=4556; p<0.001).

IQ and adaptive function (<xref rid="T2" ref-type="table">Table 2</xref>):

Cognitive abilities (FSIQ or GCA) were available for 56 children (13 children of +MAR mothers; 43 children of −MAR mothers). Adaptive scores were available for 44 children (10 in +MAR; 34 in −MAR). There were no significant differences in IQ, global adaptive function, or any of the adaptive subscales between the two groups. Additionally, verbal and nonverbal IQ did not differ based on mother’s MAR positivity.

Other behaviors:

Behaviors from the ABC (Table 2) were available for 38 children (9 in +MAR; 29 in −MAR). There were no significant differences in Irritability, stereotypy, hyperactivity, or other atypical behaviors.

DISCUSSION

Findings from this pilot sample document the presence of MAR ASD specific patterns in 2 geographically distinct DBPNet sites, in addition to Northern California. MAR ASD pattern positivity may be associated with more severe ASD behaviors, which is supported by 2 measures of ASD symptomatology, the SCQ (parental report) and ADOS (clinician assessment). There were no differences in in IQ, adaptive function, or aberrant behaviors based on MAR status. The existence of these antibodies in various geographical areas and the association with ASD severity in this pilot study supports the potential usefulness of +MAR autoantibodies as biomarkers of ASD risk. This may have future implications for better understanding ASD etiology and the development of potential treatment for this subtype of ASD in a subset of cases.

Overall prevalence was similar to the Northern California site (~20%) 16,17, although specific MAR patterns varied slightly. The most common MAR pattern observed was CRMP1+GDA and CRMP2+ GDA; these proteins are independently involved in axon and neurite development 17,29,30. Interestingly, CRMP2+ GDA was a low frequency MAR ASD pattern in the California study (n=3)16; however, in this small study it was present in 7 samples (5 from CHOP). None of the samples were positive for NSE+STIP1, which is another fairly common pattern associated with ASD16.

Consistent with prior studies, ADOS scores were significantly higher in children of +MAR mothers16. However, there were no differences in aberrant behaviors, such as stereotypies, based on MAR status, which is inconsistent with other work17. IQ and adaptive function were not associated with MAR status, and prior studies have not reported this either. One possible explanation for the association with ASD but not IQ or adaptive function may relate to individual proteins affecting early ASD specific pathways independent from cognitive and adaptive pathways 16. For example, the presence of CRMP1 has been associated with ASD16. We have ongoing, larger studies to address this, and pattern differences for subphenotypes of ASD (such as ASD+Intellectual Disability) are emerging (manuscript in preparation).

The oldest child of a +MAR mother in the sample was 11.25 years old, and he had a 9-year-old sister with ASD. The sample tested in this study was obtained after the diagnosis of the sibling. Ten of the +MAR mothers had more than one child; half of them had other children with ASD or DD. Further study is required to ascertain why mothers develop these antibodies and how long these antibodies may persist.

One limitation is that plasma samples were collected up to 14 years after birth, so it is unknown if MAR antibodies were present while these mothers were pregnant and prospective studies are currently underway to investigate this. If these antibodies are in fact present during pregnancy, based on the oldest child (11 years old) of a +MAR mother in our sample, it is possible that MAR antibodies can last for many years, especially since the mother’s last pregnancy (also a child with ASD) was 9 years before sample collection. There do not seem to be associations with MAR positivity and IQ/developmental function, aberrant behaviors, or adaptive function in these associative studies. This, however, is limited by our small sample size in this pilot study, so we may be underpowered to assess these developmental characteristics. Another limitation is our reliance on a clinical sample. Due to practice variations in clinical diagnostic assessments, data was not collected or was missing for various assessments, such as the ABC. In addition, we cannot prove that the observed MAR ASD patterns are indeed ASD-specific since this study did not include mothers of typically developing or developmentally delayed children without ASD as a comparison. Our small sample also limited our ability to look at behavioral characteristics related to specific MAR autoantibody patterns.

One strength of our study is that all children were diagnosed with ASD in a standardized manner (DSM, ADOS, along with expert clinical judgement). Another strength of this study is geographic diversity of the sample groups and utilization of the DBPNet network.

Future endeavors will include investigation of the clinical utility of including MAR autoantibody patterns as part of the etiologic evaluation of children clinically diagnosed with ASD, as a secondary screening tool for young children with developmental/behavioral concerns who have not yet been referred for diagnostic evaluation, or targeted screening for younger at-risk siblings of children with ASD. Our data also support the possible association of MAR positivity with ASD severity ratings and an established protocol for uniform data collection across sites for a larger multi-site study in the future. Further investigation is needed to better understand the mechanistic processes related to MAR pathogenicity on neuronal development, and pre-clinical models are currently underway. In summary, we have preliminarily identified the presence of MAR ASD in 2 distinct geographic locations (in addition to California) and found associations with ASD severity that support MAR ASD as a meaningful subtype of ASD likely present across a variety of geographic areas.

Acknowledgments

DBPNet is supported by cooperative agreement 1UT5MC42432-01-00 from the Maternal and Child Health Bureau, Health Resources and Services Administration, U.S. Department of Health and Human Services. This information or content and conclusions are those of the author and should not be construed as the social position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government.

The data used for this study was collected as part of the DBPNet. Data was contributed by the following DBPNet member sites: UC Davis MIND Institute (Angkustsiri), University of Arkansas for Medical Sciences (Fussell), Children’s Hospital of Philadelphia (Bennett).

The following individuals contributed as members of the DBPNet Steering Committee, By Institution: Albert Einstein College of Medicine/Children’s Hospital at Montefiore Medical Center: Ruth Stein; Baylor College of Medicine: Robert Voigt; Boston Children’s Hospital, Harvard School of Medicine: William Barbaresi; Boston Medical Center: Marilyn Augustyn; Children’s Hospital Colorado: Sandra Friedman; Children’s Hospital Los Angeles: Douglas Vanderbilt; Children’s Hospital of Philadelphia: Nathan Blum; Cincinnati Children’s Hospital Medical Center/University of Cincinnati: Susan Wiley; Hasbro Children’s Hospital/Brown Medical School: Pamela High; Kansas City Developmental Behavioral Pediatrics (KC-DBP) Consortium: Cy Nadler; Lucile Packard Children’s Hospital: Heidi M. Feldman; NYU Grossman School of Medicine: Alan Mendelsohn; Rainbow Babies and Children’s Hospital: Nancy Roizen; University of Oklahoma Health Sciences Center: Ami Bax.

NIEHS Center for Children’s Environmental Health (2P01ES011269–11) and EPA grants (83543201), NIEHS-CHARGE study (R01ES015359), and the NICHD IDDRC 054(U54HD079125).

The Center for Excellence in Developmental Disabilities at the UC Davis MIND Institute is funded by the Administration on Intellectual and Developmental Disabilities (AIDD) of the U.S. Department of Health and Human Services (HHS) under grant number 90DD0823.

MIND Institute Intellectual and Developmental Disabilities Research Center (P50 HD103526) We thank Daniel Tancredi, PhD, for statistical consultation and review of this manuscript.

Disclosure: The authors declare no conflict of interest.

REFERENCESMaennerMJ, ShawKA, BakianAV, Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018. MMWR Surveill Summ. 2021;70(11):1–16.GuoH, WangT, WuH, Inherited and multiple de novo mutations in autism/developmental delay risk genes suggest a multifactorial model. Molecular autism. 2018;9(1):1–12.29321841Hertz-PicciottoI, CroenLA, HansenR, The CHARGE study: an epidemiologic investigation of genetic and environmental factors contributing to autism. Environmental health perspectives. 2006;114(7):1119–1125.16835068ChasteP, LeboyerM. Autism risk factors: genes, environment, and gene-environment interactions. Dialogues in clinical neuroscience. 2012;14(3):281.23226953BaumanMD, Van de WaterJ. Translational opportunities in the prenatal immune environment: Promises and limitations of the maternal immune activation model. Neurobiol Dis. 2020;141:104864.32278881BoksaP Effects of prenatal infection on brain development and behavior: a review of findings from animal models. Brain Behav Immun. 2010;24(6):881–897.20230889EdmistonE, AshwoodP, Van de WaterJ. Autoimmunity, Autoantibodies, and Autism Spectrum Disorder. Biol Psychiatry. 2017;81(5):383–390.28340985BrimbergL, SadiqA, GregersenPK, Brain-reactive IgG correlates with autoimmunity in mothers of a child with an autism spectrum disorder. Mol Psychiatry. 2013;18(11):1171–1177.23958959BraunschweigD, DuncansonP, BoyceR, Behavioral correlates of maternal antibody status among children with autism. Journal of autism and developmental disorders. 2012;42(7):1435–1445.22012245DaltonP, DeaconR, BlamireA, Maternal neuronal antibodies associated with autism and a language disorder. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society. 2003;53(4):533–537.ZimmermanAW, ConnorsSL, MattesonKJ, Maternal antibrain antibodies in autism. Brain, behavior, and immunity. 2007;21(3):351–357.17029701SingerHS, MorrisCM, GauseCD, Antibodies against fetal brain in sera of mothers with autistic children. Journal of neuroimmunology. 2008;194(1–2):165–172.18093664BrimbergL, MaderS, JeganathanV, Caspr2-reactive antibody cloned from a mother of an ASD child mediates an ASD-like phenotype in mice. Mol Psychiatry. 2016;21(12):1663–1671.27698429MartinLA, AshwoodP, BraunschweigD, Stereotypies and hyperactivity in rhesus monkeys exposed to IgG from mothers of children with autism. Brain, behavior, and immunity. 2008;22(6):806–816.18262386JonesKL, PrideMC, EdmistonE, Autism-specific maternal autoantibodies produce behavioral abnormalities in an endogenous antigen-driven mouse model of autism. Mol Psychiatry. 2020;25(11):2994–3009.29955164Ramirez-CelisA, BeckerM, NunoM, Risk assessment analysis for maternal autoantibody-related autism (MAR-ASD): a subtype of autism. Mol Psychiatry. 2021.BraunschweigD, KrakowiakP, DuncansonP, Autism-specific maternal autoantibodies recognize critical proteins in developing brain. Transl Psychiatry. 2013;3:e277.23838888PruetteJR. Autism diagnostic observation schedule-2 (ADOS-2). Google Scholar. 2013.LordC, RutterM, DiLavorePC. Autism Diagnostic Observation Schedule--Generic. Dissertation Abstracts International Section A: Humanities and Social Sciences. 1999.RutterM, BaileyA, LordC. The social communication questionnaire: Manual. Western Psychological Services; 2003.OzonoffS, WilliamsBJ, LandaR. Parental report of the early development of children with regressive autism: the delays-plus-regression phenotype. Autism. 2005;9(5):461–486.16287700SparrowSS, BallaDA, CicchettiDV, Vineland adaptive behavior scales. 1984.AmanMG, SinghNN, StewartAW, The aberrant behavior checklist: a behavior rating scale for the assessment of treatment effects. American journal of mental deficiency. 1985.RoidGH, PomplunM. The stanford-binet intelligence scales. The Guilford Press; 2012.WechslerD Wechsler intelligence scale for children–Fourth Edition (WISC-IV). San Antonio, TX: The Psychological Corporation. 2003.ElliottCD, MurrayG, PearsonL. Differential ability scales. San Antonio, Texas. 1990.MullenEM. Mullen scales of early learning. AGS Circle Pines, MN; 1995.Ramirez-CelisA, EdmistonE, SchauerJ, Peptides of neuron specific enolase as potential ASD biomarkers: From discovery to epitope mapping. Brain Behav Immun. 2020;84:200–208.31812776MakiharaH, NakaiS, OhkuboW, CRMP1 and CRMP2 have synergistic but distinct roles in dendritic development. Genes Cells. 2016;21(9):994–1005.27480924AkumBF, ChenM, GundersonSI, Cypin regulates dendrite patterning in hippocampal neurons by promoting microtubule assembly. Nat Neurosci. 2004;7(2):145–152.14730308MAR-ASD patterns observed

Gray shaded subjects are from the ACHRI cohort.

CRMP1: collapsin response mediator protein 1; CRMP2: collapsin response mediator protein 2; GDA: guanine deaminase; STIP1: stress-induced phosphoprotein-1; YBOX: Y-box binding protein 1; LDHA: lactate dehydrogenase A; NSE: neuron-specific enolase

ADOS comparison and SCQ scores in −MAR and +MAR groups

Demographic characteristics

CHOP n=33ACHRI n=35Total=68p-value
+MAR7/33 (21%)9/35 (26%)16/68 (23.5%)0.75 (Fisher’s) OR 0.78 95%CI (0.21; 2.77)
Mother’s age at time of blood draw (yrs)
Mean (sd)40.5 (6.2)36.8 (5.4)38.5 (6.1)0.014
Range26–5427–4726–54
Child age (yrs)
Mean (sd)8.3 (3)8.2 (2.4)8.3 (2.7)n.s.
Range2.6–14.13.2–11.82.6–14.1n.s.

IQ, Adaptive Function, and Aberrant Behaviors

IQ −MAR (n=43 of 52) +MAR (n=13 of 16) Fisher exact p-value
FSIQ (Full Scale IQ) or GCA (General Conceptual Ability)0.93
 Average or above (>85)18 (42%)6 (46%)
 Low Average (70–84)12 (28%)4 (31%)
 Extremely Low (<69)13 (30%)3 (23%)
Vineland −MAR (n=34) +MAR (n=10) Wilcox p-value
Adaptive Behavior Composite70.3 ± 9.467.7 ± 9.10.42
Communication75.1 ± 12.372.2 ± 15.50.57
Daily Living Skills72.4 ± 11.467.9 ± 11.30.16
Socialization68.2 ± 1167.8 ± 90.89
Motor78.7 ± 9.577.3 ± 10.20.85
Aberrant Behavior Checklist −MAR (n=29) +MAR (n=9) Wilcox p-value
Total50.9 + 33.638.2 + 170.51
Irritability12.8 + 9.97.1 + 4.80.16
Lethargy9.4 + 107.7 + 7.30.88
Stereotypy5.9 + 55.4 + 40.95
Hyperactivity18.7 + 1114.3 + 6.90.27
Inappropriate Speech4 + 3.73.6 + 2.90.93