Dimensional Structure of the Autism Phenotype: Relations Between Early Development and Current Presentation
- First Online:
- Cite this article as:
- Kamp-Becker, I., Ghahreman, M., Smidt, J. et al. J Autism Dev Disord (2009) 39: 557. doi:10.1007/s10803-008-0656-5
- 244 Views
The dimensional structure of higher functioning autism phenotype was investigated by factor analysis. The goal of this study was to identify the degree to which early symptoms of autism (measured using the ADI-R) could be predictive of the current symptoms of autism as identified using the ADOS, the adaptive behavior scales, IQ scores and theory of mind scores. Participants included 140 subjects with Full Scale IQ > 70 (104 with autism spectrum diagnosis, 36 with non autism diagnosis, age range 6–24). For the early development as well as for the current presentation a multi-factor solution was found. In line with other studies we found that the social interaction and communication domains are closely related to one factor namely: Social communication. An additional factor implies anxious and compulsive behavior which is associated with current social communication functioning.
KeywordsAutism spectrum disordersDimensional structure of autism phenotypeHigh-functioning autismAsperger syndromeAdaptive behavior
Current studies of the dimensional structure of autism spectrum disorders: whole symptomatology
Age (mean/SD or range)
IQ (mean/SD; range)
Methods: statistical procedure and instruments
Results: factors (accounted variance)
Tanguay et al. (1998)
Autism, Asperger, PDD-NOS
80 month/33 month
14 subjects: no data available
49 subjects: 79/27; 29–143
25 ADI-R items
Theory of mind
(50.5% of variance)
Robertson et al. (1999)
Autism, Asperger, PDD-NOS
88 month/–/36 months to 191 months
6 < 70
30 ADOS items of communication and social interaction, correlations to the ADI-R factors
Theory of mind
(72% of variance)
Bölte and Poustka (2001)
Autism, atypical autism
5–49 years (14.4/8.4)
For 139 subjects: 77.1/27.3; 27–136
For 99 subjects: ~20–85
ADI-R all algorithm items
Social communication I
Social communication II
(46.1% of variance)
Szatmari et al. (2002)
62 low functioning
67 high functioning
Low functioning: 11.6/5.8
High functioning: 5.5/0.9
3 domains of ADI-R
VABS scores for communication, activities of daily living, socialization
Level of functioning
69.8% of variance
Tadevosyan-Leyfer et al. (2003)
98 ADI-R items
Constantino et al. (2004)
80 PDD (autism, AS, PDD-NOS)
6 unspecific developmental disorder
35 other Non-PDD
Factor analysis and cluster analysis
63 items from ADI-R (N = 44)
SRS (maternal report N = 168)
SRS (teacher report N = 61)
Singular, continuously distributed underlying factor
27–40% of variance
130 Autism, AS, PDD-NOS
125 non-autism; 110 autism, 153 PDD-NOS/Asperger
Range: 4–20; 4–24
55/23 20–129; 35–152
Factor analysis: ADI-R algorithm domain items
Impaired social communication
Impaired play skills
Georgiades et al. (2007)
109 month/65 month
26 subjects: no data available
Factor analysis and correlations with independent variables: ADI-R algorithm domain items, IQ, VABS
Inflexible language and behavior
Repetitive sensory and motor behavior (50% of variance)
912 autism, 439 non-autism, 279 developmental delays; 970 autism, 98 nonautism ASD, 214 non-ASD developmental delays
14–82 month; 18 month to 16 years
Nonverbal IQ mean ~77; ~79
Factor analysis: ADOS items
Restricted, repetitive behaviors
An empirically developed dimensional approach that defines the spectrum on multiple dimensions may offer several advantages. It may, for example, result in more correspondence between the results of genetic research and the phenotype of autistic disorders, provided the pathology can be summarized by empirical and valid behavior dimensions (Volkmar et al. 2004; van Lang et al. 2006; Hus et al. 2007).
Another consideration is the relation between the autistic symptoms and other behavioral features like adaptive behavior, intelligence or age. The relationship between adaptive functioning, autism symptomatology and intelligence remains unclear (Klin et al. 2007; Saulnier and Klin 2007). Some studies show that intelligence and language abilities are more appropriate predictors for lower functioning individuals with autism than for the higher end of the cognitive spectrum present in autism (Nordin and Gillberg 1998; Howlin 2000, 2003; Howlin et al. 2004; Szatmari et al. 2003; Tsatsanis 2003; McGovern and Sigman 2005). Outcome studies of high functioning individuals with autism seem to indicate that higher intellectual potential is not a good predictor of adaptability in adulthood (Howlin 2003). The ability to transfer cognitive potential into real-life skills is often considerably impaired (Bölte and Poustka 2002; Klin et al. 2007; Saulnier and Klin 2007) and the scores for adaptive behavior in daily life are substantially below the verbal-IQs “highlighting the magnitude of adaptive impairment despite cognitive potential” (Klin et al. 2007, p. 748). The few existing studies have revealed that early language and nonverbal skills are important predictors of outcome regarding communication and social domains (Szatmari et al. 2003), a negative correlation regarding age was established, but no relationship to the current symptomatology of autism (Klin et al. 2007; Saulnier and Klin 2007).
An empirically developed and valid dimensional structure of the symptomatology may also give better indicators of an individual’s progress because it has considerable implications for intervention programs. Factors that enhance the acquisition of adaptive skills have to be found to optimize treatment. If we can identify the autistic symptoms that influence the adaptive skills, we would have an implication for treatment options to improve the adaptive skills in daily life. “This information needs to be used as a platform for research to clarify what factors truly can predict real life success. This issue has received much less attention than it should, both scientifically and clinically “(Klin et al. 2007, p. 758).
Current studies of the dimensional structure of autism spectrum disorders: restricted, repetitive behaviour and interests domain
Age (mean/SD or range)
IQ (mean/SD; range)
Methods: statistical procedure and instruments
Results: factors (accounted variance)
Cuccaro et al. (2003)
109 month/55 month
Factor analysis and correlations: 12 ADI-R items from the restricted, repetitive behaviour and interests domain
VABS sum score
Repetitive sensory motor actions with a negative correlation to index of adaptive functioning
Resistance to change
(60.7% of variance)
Carcani-Rathwell et al. (2006)
573 PDD (autism, atypical autism, Asperger, PDD-NOS)
119 mental retardation in the absence of psychiatric diagnosis
390 normal intelligence
302 mental retardation
Model’s goodness to fit-Testing: Repetitive and stereotyped behaviours item sheet
Repetitive movements and sensory behaviours are generally associated with lower developmental age and less specific to the autistic syndrome
Cognitive rigidity (higher order group) more autism specific
Szatmari et al. (2006)
PDD (autism, Asperger, atypical autism or PDD-NOS)
101 month/66 month
Factor analysis: 11 ADI-R items from the restricted, repetitive behaviour and interests domain Correlations with Vineland and other ADI-R domains
Insistence of Sameness, positively correlated with autistic symptoms in the communication and language domain
Repetitive sensory and motor behaviours, associated with lower level of functioning (33(ever)–36 (current)% of variance)
Richler et al. (2007)
165 Autism Spectrum Disorder ASD (117 autism, 48 PDD-NOS), 49 non spectrum developmental disorders DD, 65 typical development TD
<3; ASD: 29 month, DD: 27 month, TD: 20 month
ASD: 49, DD: 67, TD: 113
Factor analysis: ADI-R items from the restricted, repetitive behaviour and interests domain
Repetitive sensorimotor factor
Insistence on sameness
As shown in Tables 1 and 2, the results of the studies differ in quantitative aspects: This range from only one to six underlying factors. The studies also differ in respect to the content of the resulting factors. One reason may be that there are notable methodological differences among these studies. This concerns the sample size ranging from 51 up to 1,259. The variety of the sample in respect of age and IQ levels also differs: From 80 month old children up to the age of 47, from very low to high IQ’s. The sampling method differs also: Some studies only recruited affected subjects with a diagnosis of autism, some included subjects from the whole spectrum and some studies also included children with normal development or individuals with other diverse psychiatric diagnosis.
Some of the different results of these studies may be explained by another methodological argument: The selection of the item pool for analysis. This may be influenced in some studies by sample size: To reduce the number of variables for the factor analysis only the algorithm items of the ADI-R were chosen. But this is circular argumentation: The algorithm items of the ADI-R were selected due to fact that they most “closely depicted the specific abnormalities described in the clinical descriptions and diagnostic guidelines from the DSM-IV and ICD-10” (Lord et al. 1994). Another limitation is given by the fact that for the development of the algorithm of ADI-R “diagnostically borderline cases had been excluded” and “subjects with PDD-NOS were grouped with other non-autistic subjects” (Lord et al. 1997). So questions arise whether the results are valid for the whole spectrum especially for the high functioning subgroup. Most of the presented results were obtained by factor analysis, but one difficulty with factor analysis is that the characteristics of the sample will profoundly influence the results. Hence it is important to determine whether the factor structure is stable across various subgroups of autism.
There are also some noticeable similarities in these studies. These similarities include in particular the differentiation (as in ICD 10 and DSM-IV) of the symptom categories “communication” and “social interaction”: this differentiation was definitely not found in four studies. There is empirical evidence that these two symptoms categories can not be separated as supposed in ICD 10 and DSM-IV. In the domain of restricted, repetitive behavior there is support that “resistance to change” or “cognitive rigidity” differs from “repetitive sensory motor actions”.
On the one hand the objective of this study is to examine a comprehensive, empirically developed dimensional structure of the autism phenotype by investigating a specific sample of ASD and comparing these results with the existing results of other studies. On the other hand we intended to examine the coherence to other behavioral features like adaptive behavior, age and intelligence (IQ). The clinical relevance of the developed dimensional structure will be tested by examining the connections to other factors (adaptive functioning, theory of mind abilities, age and IQ). The main focus is on the predictive power of early development factors to current functioning. In this way we hope to understand what the emerged factors mean to us. Most previous studies have done this by examining the whole spectrum of autistic disorders and by choosing only the algorithm items of the diagnostic instruments. In our study all items are included in the analysis because it is not yet clear which items are specific to the high end of the autistic spectrum.
Our sample included altogether 140 subjects, six of whom were female. The age ranged from 6 to 24 years and the Full Scale IQ from 70 to 139. 52 of them were diagnosed as having Asperger syndrome, 44 as high-functioning autism and 8 as atypical autism, 36 had a non-autism diagnosis. It is important to stress the fact that all subjects came to our clinic with the suspicion of autism. This means that even the subjects without a diagnosis of autism showed considerable “autism-like” behavior. This is reflected by the fact that the non-autism diagnoses are principally differential diagnoses of autism like ADHD (n = 18), emotional disorder of childhood (n = 6), receptive speech disorder (n = 3), schizoid personality disorder (n = 3), other personality disorder (n = 2), delay of development (n = 2) and learning disability (n = 2). Our study therefore examines the “higher functioning end” of the autistic spectrum and its related diagnoses.
Sample descriptive statistics
Best estimate diagnosis
Age at testing time
The data presented in this paper originates from a research project on high-functioning autism and Asperger syndrome which included a comprehensive diagnostic and neuropsychological testing as well as genetic components. All of the participating families gave their written informed consent before participating in the study. The study was approved by the Ethical Committee of the Medical Faculty of our university.
The participating subjects were diagnosed by experienced clinicians according to the standard Criteria of ICD-10 (World Health Organization 1993) and DSM-IV (American Psychiatric Association 1994) and underwent extensive psychiatric examination. The expression of autistic symptoms was assessed by the German version of the Autism Diagnostic Observation Scale (ADOS-G; Lord et al. 2000; Rühl et al. 2004) and a semi-structured autism specific parent interview (ADI-R; Lord et al. 1997; Bölte et al. 2006); this in-depth assessment was conducted by trained examiners (I. K.-B., M. G).
The scoring instructions of the ADI-R cover two points of time: “current” (within 3 month before the interview) and “at age of 4–5 or ever” (behaviors had not developed by age 4–5 years or throughout the individual’s life). In the present study only the judgments concerning the “at age 4–5 or ever” were included in the analysis, so the focus is on early development.
The ADOS-G consists of four modules, appropriate for children and adults of differing language levels, and which range from nonverbal to verbally fluent. For the purposes of this study only Module 3 and 4 for verbally fluent children and adolescents were used. Furthermore the inter rater reliability between pairs of raters was investigated using 17 videotaped ADOS-G assessments: The kappa values ranged from 0.42 to 1.0. The mean of the kappa values was 0.75 and complete agreement was established for autism/non-autism distinction.
The Vineland Adaptive Behavior Scales (VABS) (Sparrow et al. 1984) is an interview-based tool assessing adaptive behavior in three domains: communication (receptive, expressive, and written), daily living skills (personal, domestic, and community), and socialization (interpersonal relations, play and leisure time, and coping skills). The VABS is sensitive to severity of impairments in ASD (Carter et al. 1998; Klin et al. 2007; Saulnier and Klin 2007).
For intellectual testing the German version of the Wechsler Intelligence Scales was chosen. These are a series of standardized tests used to evaluate cognitive abilities and intellectual abilities in children (WISC-III; Tewes et al. 1999) and adults (WAIS-R; Tewes 1993). The theory of mind abilities are evaluated by a battery of tests which highlights relevant aspects of theory of mind and associated social perception: face recognition (Benton et al. 1983); facial emotion recognition (www.candit.com: Facial Emotion Matching); and social attributions (social attribution test, Klin and Jones 2006). The number of correct answers in all three tests was summarized to one value.
For each patient, DSM-IV/ICD-10 psychiatric diagnosis had been established by at least two expert clinicians among the authors. Subjects with an autistic diagnosis fulfilled the cut-off criteria of the ADOS-G and ADI-R for autism or ASD.
To examine the dimensional structure of the autism phenotype the data from the ADI-R and the ADOS-G was selected for analysis. We used all relevant items of the ADI-R and the ADOS-G which display the pathology of our high functioning sample regardless of their inclusion in the diagnostic algorithm or not. The criteria for judging items as relevant was that the item measured the autistic symptomatology itself (not comorbidity or general behavior) and was judged as irrelevant if more than 90% of the sample coded with a zero (i.e. that the behavior shows no evidence of abnormality). A test of sampling adequacy establishing whether the partial correlations among variables are too small was obtained (Kaiser–Meyer–Olkin test). The Bartlett’s test of sphericity (establishing whether the correlation matrix is an identity matrix, which would indicate that the factor model is inappropriate) was also undertaken. Principal components analyses were used. These are a statistical technique that transforms an original set of variables into a smaller set of uncorrelated variables or factors that represent most of the information of the original variables (Morrison 1990). The analysis was done with the FACTOR procedure from the software packages SPSS for Windows (Version 14.0). For all principal-components analyses, VARIMAX rotation (Kaiser 1958) was used to achieve a simpler structure while maintaining independence between the rotated factors. Use of PROMAX rotation yielded results similar to those reported below. To evaluate the extent to which the obtained factors were correlated with other variables, we calculated scores based on each of the derived factors for every participant. Optimally weighted factor scores were automatically generated by SPSS so that the mean factor score for the entire sample was zero and the standard deviation was one. We used Pearson correlation coefficients (level of significance ≤ 0.01) to examine the association between the empirically derived factors, IQ and age.
To establish which variables predict the current pathology and the current adaptive behavior abilities best, a linear regression analysis was calculated. The emerged factors of the ADOS-G were used as criteria variables and in another analysis the Vineland data. The emerged ADI-R-Factors—which present the pathology of the early development—the verbal IQ, performance IQ, the theory of mind-skills variable and age were used as predictor variables.
Factor Analysis ADI-R: Early Development
The Items “loss of verbal abilities”, “comprehension of speech”, “general level of speech”, “actual communicative speech”, and “midline hand movements” were excluded because of irrelevance for a majority (>90%) the sample (see above). Altogether 47 items of the ADI-R were considered in the factor analysis.
Factor loadings on social-communication, anxiety and compulsions, stereotyped behavior and inadequate behavior
Factor 1: social communication (SOCOM early)
Imitation of activities/play
Pointing to express interest
Group play with peers
Showing and directing attention
Quality of social overtures
Interests in children
Imitative social play
Imaginative play with peers
Responses to approaches of other children
Offering to share
Appropriateness of social responses
Initiation of appropriate activities
Factor 2: anxiety and compulsions (anx)
Resistance to change in environment
Resistance to change in daily routines
Unusual negative reaction to sensory stimuli
Sensitivity to noise
Repetitive use of objects or interest in parts of objects
Unusual attachment to objects
Factor 3: stereotyped behavior (stereo)
Use of other’s body to communicate
Stereotyped utterances and delayed echolalia
Unusual sensory interests
Factor 4: inadequate behavior (inad)
Inappropriate questions or statements
Range of facial expressions
Inappropriate facial expression
Attention to voice
The first factor is named: Social communication (“SOCOM early”), it explains 17.72% of variance. 21 out of 47 items with high loadings are included in this factor and are mainly items of the algorithm of the ADI-R. This factor combines items from the original social interaction (13 items) and communication domain (8 items) of the ADI-R. It contains play and imitativebehavior, nonverbal behavior as well as reciprocal behavior. There is no correlation to Full Scale IQ. The second factor is named: anxiety and compulsions (anx), 10 items with high loadings are included and it explains 10.74% of variance. It is dominated by anxiousness and compulsive behavior but also circumscribed interests (which in some cases have a compulsive character). Interestingly some other aspects of verbal behavior are included: verbal rituals and neologism. No correlation to IQ was found. The third factor (eight items, 7.54% of variance explained) is characterized by stereotyped behavior (stereo) in aspects of nonverbal as well as verbal behavior. The circumscribed interests load negatively on this factor. There is a significant negative correlation to full scale IQ (−0.30, Pearson coefficient, p < 0.01). The fourth factor (explaining 6.38% of variance) is best described by inadequate behaviors (inad) (six items).
The comparison of the autism and the non-autism group shows that especially the first two factors differ considerably (p-value = 0.00 for factor “anxiety and compulsions”, p = 0.01 for “SOCOM early”).
Factor Analysis ADOS-G: Current Presentation
Factor loadings of the ADOS-G
Factor 1: social communication (SOCOM current)
Overall quality of rapport
Reporting of events
Quality of social overtures
Empathy/comments on others emotions
Amount of reciprocal social communication
Quality of social responses
Asks for information
Factor 2: non/verbal behavior
Unusual eye contact
Factor 3: hyperactivity
Factor 4: stereotyped behavior
Factor 5: interests and compulsions
Compulsions or rituals
Overall level of language
Analog to the results of the ADI-R, the first factor includes 13 items of the communication and social interaction domain, mainly algorithm items. So the first factor is also named “Social communication (SOCOM current)”, which explains the main part (26%) of variance. Similar to the results of the ADI-R play behavior is also included (item “imagination”). No correlations to IQ and age were found.
The second factor is mainly influenced by eye contact and speech abnormalities and is named: “non/verbal behavior”. This factor explains 10% of variance. There was no significant correlation to IQ. The third factor explains 8% of variance and is named “hyperactivity”. This factor is mainly influenced by the item “hyperactivity” while “anxiety” loads negatively meaning that anxious behavior correlates negatively with hyperactivity. Participants with high hyperactivity show low anxiety. This factor reveals a significant correlation to age (−0.46 Pearson coefficient, p < 0.01) demonstrating that the older the subjects are the lower hyperactivity is. The fourth factor is named “stereotyped behavior” (7% of variance) which correlates significantly with the performance IQ (−0.26 Pearson coefficient, p = 0.01). The last factor “interest and compulsions” explains 5% of variance.
Correlations (Pearson coefficients) between the ADOS and ADI factors
Anxiety and compulsions
Interests and compulsions
Vineland Adaptive Behavior Scales
Our sample had a mean full IQ of 100.90 (with a standard deviation of 18.31) and a mean sum score of “adaptive behavior composite” of 63.24 (SD = 24.60) for the whole sample. However all scores indicate a great deal of scatter, especially the communication domain scores. The adaptive skills are more than three standard deviations below the cognitive abilities, which make abundantly clear that there is a high magnitude of social disability despite good cognitive abilities.
Regression Analysis: Relations Between Early Development and Current Presentation
Results of linear regression analysis
Criteria variables: current presentation
ADOS SOCOM actual
Anxiety and compulsions
Theory of mind
Daily living skills
Anxiety and compulsions
One result of the first regression analysis confirms that the regression model for the sub-sample of the autism subjects (F = 2.748, p = 0.010) attained significant explanation, but accounted only for a small amount of explained variance (R2 = 0.209, adjusted R2 = 133) The amount of autistic symptoms in the current presentation is best predicted by the amount of anxiety and compulsions in the early development, in combination with age and theory of mind abilities. Of interest is the fact that age and theory of mind abilities are of relevance for the actual autistic symptoms only in combination with the factor “anxiety and compulsions”. There was no significant correlation between this factor and age alone. The factor SOCOM of the ADOS is higher the more symptoms of “anxiety and compulsions” are shown in early childhood, the older the child and the lower theory of mind abilities are. The factor “anxiety and compulsion” also predicts the noticeable problems in nonverbal and verbal behavior (unusual eye contact, speech abnormalities, reduced facial expression and stereotyped phrases) but this relation is not of satisfying significance (p = 0.025), while hyperactivity is best predicted by age.
For the adaptive behavior the regression model for the sub-sample of the autism subjects (F ≥ 4.132, p = 0.000) shows significant explanation and the amount of explained variance is greater (R2 = 0.287–0.301, adjusted R2 = 0.218–0.230). The abilities of the autistic subject in everyday life in the domain of communication are best predicted by the verbal-IQ, in the domain of daily living skills they are best predicted by the autistic symptoms of early development (factor “SOCOM early”) and by the factor “anxiety and compulsion”. Socialization is best predicted by the age of the subject, the relation to the autistic symptoms of early development (factor “SOCOM early”) is once again not sufficient (p = 0.020).
The results of our analyses suggest that the Asperger/HFA phenotype is structured by 4 respectively 5 dimensions which differ to the conceptualization of DSM-IV or ICD-10. The results of the factor analyses account for a sufficient amount of variance (42% for ADI-R, 57% for ADOS-G). In other studies (Table 1) mostly a three factor solution was chosen but furthermore with this differentiated factor structure some important relations to other variables could be identified.
Dimensional Structure of the Symptoms of Early Development
The ADI-R Factor “SOCOM early” of our study is predominantly identical to the factor SOCOM of the Canadian study (Georgiades et al. 2007) with the exception of only four items (“use of other’s body to communicate”, “nodding”, “head shaking”, and “inappropriate facial expression”) which are included in other factors in our study and one additional subdomain (“total, relative failure to initiate or sustain conversational interchange”). The emerged factor “SOCOM early” is also very similar to the factor: “impaired social communication skills” of the Dutch study of van Lang et al. (2006): Just the items “use of other’s body to communicate”, “inappropriate facial expression”, “nodding” and “head shaking” are not included, but the items of the Dutch study “impaired make believe and play skills” are included in our factor “SOCOM early”. In comparison with the German study (Bölte and Poustka 2001) our factor “SOCOM early” is also similar but not identical to their factor “social communication” as it also includes some items of the factor “speech” of the study of Bölte and Poustka (2001).
The factor “SOCOM early” combines the “reciprocal social interaction” and “communication” domain of the ADI-R: This result seems to be stable in several studies and is replicated in our study for a sample of high-functioning autism. Furthermore, we could demonstrate that this factor is of relevance for the adaptive behavior in daily life, especially for daily living skills. In contrast to the Canadian study (Georgiades et al. 2007) we found in our high functioning sample no correlation to IQ, neither verbal nor performance IQ.
Consistent with other studies (“Compulsions”, “resistance to change”, “Cognitive rigidity”, “Insistence of Sameness”, “insistence on sameness”, see Table 2) we found a second factor named “anxiety and compulsions”. In our study this factor also included items from the domain of communication (“verbal rituals”, “neologism”) demonstrating that compulsive behavior also influences verbal behavior. This is in accordance with the result of the study of Szatmari et al. (2006) which found a positive correlation between the factor “insistence on sameness” and autistic symptoms in the communication and language domain. It could be hypothesized that these behaviors result from anxiety and compulsions but on the other hand the diversity of behavioral aspects limits the interpretation of the factor. As a new result we could demonstrate that this factor is of high relevance for further development. It best predicts the main factor of autistic symptomatology in the current presentation. It differs highly between autism and non autism groups. This result is similar to the study of Carcani-Rathwell et al. (2006) who found that this “higher order” factor is more autism specific. Another important result is that despite its demonstrated relevance only two out of ten items of this factor are included in the algorithm of the ADI-R. This could be an indication that further validation studies in support of the ADI-R algorithm are necessary for high-functioning autism subjects.
The third factor also combines aspects of stereotypes and communication in the general aspect of stereotyped behavior. Here, the influence of intelligence becomes evident, as the circumscribed interests correlate with IQ itself, they load negatively on the factor stereotyped behavior. There is a considerable relation to the cognitive abilities and fewer to the autistic specific symptomatology as represented in other factors. This is also reflected by the fact that the autism group doesn’t differ from the non autism group in aspects of stereotyped behavior. In the existent studies such a factor was not found: Indeed the factors “stereotyped language and behavior” (van Lang et al. 2006) and “inflexible language and behavior” (Georgiades et al. 2007) seem to be similar, but in both studies these factors imply circumscribed interests and compulsive behavior. This is possibly explained by the influence of the cognitive abilities which are relatively high in our sample. Another interesting fact is the result that gestures are not included in this factor but in the main factor both in the analysis of ADI and ADOS.
In summary the amount of variance explained in our study as well as in all other studies in this area is only moderate. One could argue that the usefulness of the 4 factor solution for the ADI-R analysis is disputable: it potentially encompasses a variety of behavior that does not represent any single entity. But by demonstrating that the derived factors are of relevance for the actual symptoms and adaptive behavior, the usefulness becomes evident.
Dimensional Structure of the Symptoms of Current Presentation
Again we found a highly autism specific factor, which includes items from the social interaction and communication domain of the ADOS-G and also play behavior. Similar to the results for ADI-R there is no correlation to IQ. This factor is very similar to the factor analysis of ADOS data on large independent datasets recently published (Gotham et al. 2007, 2008) named “social affect”. Only the item “unusual eye contact” loads in our study on a separate factor. The second factor shows similarities to the factor “affective reciprocity” of Robertson et al. (1999) which included the items “facial expression” and “speech abnormalities” but the item “eye contact” loaded on another factor (joint attention). In summary we could also confirm some aspects of the few existing studies of the dimensional structure of the ADOS data.
We did not find a correlation between the main social communication factors of ADOS and ADI (see Table 6). This may be caused by the fact that social communication skills change greatly from age 4–5 upwards, even though many of the subjects still have sufficient problems in this domain. Another objection may be that the parent’s recollection of the age 4–5 behaviors is simply inaccurate after a long time period. No other study has investigated the relation between ADOS and ADI factors, further studies are needed to answer these open questions.
Predictive Power of Early ADI Factors to Current ADOS Factors and Adaptive Functioning
The emerged factor “anxiety and compulsion” in early development is associated with current social communication functioning. One could hypothesize that anxiety and compulsive behavior inhibits the development and limits the reduction of autistic symptoms. A young autistic child with low anxiety and compulsive behavior is more able to learn and to develop adequate behaviors. Although we found only modest associations between early ADI and current ADOS scores, the impact of various behavioral aspects pictured in the factor “anxiety and compulsions” is of interest and has to be examined in further studies.
Relation to Age and Intelligence
We could demonstrate that “hyperactivity” shows a high correlation to age. In line with the studies of Saulnier and Klin (2007) and Klin et al. (2007) we couldn’t find this correlation with the specific autistic symptomatology indicating that these symptoms are stable over time. In contrast, the more symptoms of “anxious and compulsive behavior” in early childhood were shown, the older the child and the lower the theory of mind abilities are, the more autistic symptoms are actual present. Unfortunately, autistic symptoms don’t get lost with age. In combination with anxious and compulsive behavior in early childhood and low theory of mind abilities they increase. Limitations are given because our study only correlates age with the autistic symptoms and the age spectrum was only 6–24 so no long term study was undertaken. In summary the IQ doesn’t predict the main autistic symptoms. In addition to other studies (Klin et al. 2007; Saulnier and Klin 2007) these results emphasize that the IQ is not an appropriate predictor of the autistic symptomatology for the higher end of the cognitive spectrum of autism: Intelligence can predict the communicative behavior but not the severity of autistic symptoms.
Relations to Adaptive Behavior
In addition to some other studies we could demonstrate that even a sample of high-functioning autism subjects show a remarkable impairment in adaptive behavior. We could demonstrate that adaptive skills and autistic symptomatology are not independent domains as suggested in other studies: Szatmari et al. (2002) found two independent factors: autistic symptoms and adaptive behavior. Klin et al. (2007) and Saulnier and Klin (2007) found only a weak relation between adaptive behavior and the symptomatology measured by the ADOS. In contrast, we could state that adaptive behavior and autistic symptoms of early development are not independent domains.
This data supports the notion that variation in autistic symptoms is not simply a reflection of developmental level (in respect of age or cognitive abilities) but may rather represent variation in some other underlying etiological factors (Georgiades et al. 2007). Our analysis was based on a micro level of symptoms and included many variables. In this way we could demonstrate the relevance of the emerged factors. By demonstrating correlations with the variables adaptive behavior, age, IQ and theory of mind the factors represent construct validity. In line with several other studies we point out that the social interaction and communication domains are so closely related that they emerge as a single factor called: Social communication. The most important conclusion of our study is that the emerged factor “anxiety and compulsion” in early development is associated with current social communication functioning. This finding has never been reported in previous work. The implication of this is that a great effort has to be made to reduce symptoms in this domain by intervention programs in early childhood because these symptoms affect further development. Another important finding is that IQ scores do not correlate with earlier or current social communication functioning. The latter has been reported previously (Gilchrist et al. 2001; Szatmari et al. 2003; Howlin et al. 2004), but the current results are an important confirmation of this observation.
Nevertheless it is necessary to investigate the validity of the proposed factors in another study since our study has a number of limitations. First of all the entire sample size was small considering the undertaken statistical procedures: The ratio of sample size to items analyzed is the main limitation of our findings. The findings presented in this paper must be regarded as a first explorative examination of the dimensional structure of high functioning ASD. Further investigation is needed to confirm our results in a larger sample. In particular, a study focusing on the comparison of a low and a high IQ sample is needed. This could clarify whether the factors established in our study are specific for our high functioning sample. Yet another limitation is the methods used (ADOS-G, ADI-R) which were not constructed to be used as quantitative measures for dimensions or even severity. Also factor analysis is sensitive to sample size and method of ascertainment. Like the authors of many other studies we opted for VARIMAX rotation and treat ADI and ADOS scores as if they are continuous, interval data although they are ordinal. Although a check demonstrated that a PROMAX rotation gave similar results this fact also places limits on the results. An advantage of our study is the homogeneity of the sample in terms of high functioning level (IQ > 70) and the inclusion of various variables for validation. It could be pointed out that the dimensional structure of higher functioning ASD using standard measures (ADOS and ADI) generates some similarities to other studies but also some new findings: By studying the relationship between autistic symptoms, adaptive behavior and other particular characteristics we may sharpen the focus of intervention for particular developmental groups.
This research was supported by a research award of the German Max Planck association H. Remschmidt received 1999. We thank all of the families for their participation in our research program.