Receptive and expressive language phenotyping in over 62,000 autistic Individuals

Receptive and expressive language phenotyping in over 62,000 autistic Individuals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Receptive and expressive language phenotyping in over 62,000 autistic Individuals Katrine Gankin, Rohan Venkatesh, Edward Khokhlovich, Andrey Vyshedskiy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8466583/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Unraveling the phenotypic complexity of autism is essential for advancing our understanding of its underlying biology, developmental trajectories, and diverse clinical presentations. Traditional classifications of language profiles in autism distinguish three groups: 1) verbal individuals without structural language impairment, 2) verbal individuals with structural language impairment, and 3) minimally verbal individuals. However, this tripartite framework hides substantial linguistic heterogeneity. Leveraging data-driven clustering on novel comprehensive syntactic language assessment from a large cohort of over 62,414 autistic individuals aged 4–21 years, we have identified more nuanced, clinically meaningful subtypes. For receptive language, three distinct phenotypes emerged: 1) Command, limited to understanding single words and simple commands; 2) Modifier, that extends to integrating nouns with adjectives but lacks full syntactic processing; and 3) Syntactic, supporting integration of nouns with spatial prepositions and complex syntactic structures. For expressive language, four phenotypes were delineated: 1) Nonverbal, 2) Single-Word, 3) Single-Sentence, and 4) Multi-Sentence. Adopting a two-dimensional framework that separately evaluates receptive and expressive abilities offers greater precision than the conventional tripartite system. This approach better captures individual variability and can guide more targeted language interventions addressing specific strengths and deficits in each domain. Such refinement holds promise for personalized therapies and improved outcomes. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology language interventions ASD language therapy syntactic language recursive language Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and the presence of restricted, repetitive patterns of behavior 1 . Across most countries, delayed language and communication development relative to peers is the primary reason parents seek formal clinical evaluation and diagnosis 2 . Language abilities are among the most robust and stable predictors of later social 3 , 4 and educational 5 , 6 functioning. Converging evidence from multiple studies suggests that language deficits may constitute a core feature of ASD 7 – 9 , and language therapy remains among the most widely implemented interventions 10 – 19 . Accordingly, refining the classification of language phenotypes in ASD is an important goal for optimizing intervention strategies and improving developmental outcomes 20 – 24 . Conventional descriptions of communication levels in ASD distinguish three broad groups: 1) verbal individuals without structural language impairment (sound structure and grammar), 2) verbal individuals with structural language impairment, and 3) minimally verbal autistic individuals 25 . However, this tripartite framework likely oversimplifies the substantial heterogeneity in language abilities observed across autistic individuals. First, despite its widespread use, the tripartite classification has not been validated in large cohorts, leaving it unclear whether it adequately captures even broad categories of language ability. For example, Munson et al. 26 identified four latent phenotypes in an analysis of 456 autistic children: 1) Low Verbal / Low Nonverbal; 2) Low Verbal / Medium Nonverbal; 3) Medium Verbal / Medium Nonverbal, and 4) High Verbal / High Nonverbal. Although that study operationalized verbal ability as the mean of receptive and expressive language Mullen scales, and nonverbal ability as the mean of visual reception and fine motor scales, its findings nonetheless point to a more complex structure than the traditional three-group model. Second, numerous studies have demonstrated that receptive and expressive language abilities do not always align 27 – 29 and that in many autistic individuals receptive language is more impaired than expressive language 30 – 36 . These findings indicate that separately characterizing receptive and expressive language phenotypes may yield a more informative and accurate classification system. For over a decade, our group has collected longitudinal, parent-reported data on language, social functioning, and health in autistic children. This resource now comprises data from more than 100,000 autistic individuals, offering a unique opportunity to address unresolved questions regarding language phenotypes at scale. Accordingly, this study had three primary aims: (1) to characterize receptive language phenotypes; (2) to independently identify expressive language phenotypes; and (3) to evaluate whether broad receptive–expressive language groupings can be validated in a large cohort of autistic individuals. Replication of three broad groupings aligned with the traditional tripartite framework would provide empirical support for that model. In contrast, failure to identify such groupings would suggest that a two-dimensional framework, independently assessing receptive and expressive language phenotypes, offers greater precision. Methods Study Participants Participants were children and adolescents using a language therapy app that was made freely available at all major app stores in September 2015 37–41 . The app provides various structured language comprehension therapy exercises and is primarily used by caregivers of children with language impairments. Most of the caregivers are presumed to be parents. Once the app was downloaded, caregivers were asked to register and to provide demographic details, including the child’s diagnosis and age. Caregivers completed a 133-item questionnaire (77-item Autism Treatment Evaluation Checklist (ATEC) 42 (Supplementary Tables 1–4); 20-item Mental Synthesis Evaluation Checklist (MSEC) 43 (Supplementary Table 5); 10-item screen time checklist 27 ; 25-item diet checklist 44 ; and 1-item parent education survey) approximately every three months. All fifteen available language comprehension items from the 133-item questionnaire were included in the cluster analysis as in previously published articles 45 – 47 (Table 1 ). In addition, all 11 available expressive items were included in the cluster analysis (Table 2 ). Response options were: very true (0 points), somewhat true (1 point), and not true (2 points). Table 1 Three language comprehension mechanisms—Syntactic, Modifier, and Command—have been identified in previous studies 45 – 47 . The language comprehension items are presented exactly as surveyed with parents in both this and earlier studies. Response options were: very true, somewhat true, and not true. Items 1 to 3 were assessed as part of the Expressive Language ATEC 42 subscale 1; the rest of the items were part of the MSEC subscale 43 . Language comprehension items (verbatim) Abbreviations used in dendrograms Command Mechanism 1 Knows own name Knows Name 2 Responds to ‘No’ or ‘Stop’ No and Stop 3 Responds to praise Resp. to Praise 4 Can follow some commands Commands Modifier Mechanism 5 Understands some simple modifiers (i.e., green apple vs. red apple or big apple vs. small apple) Color or Size / Modifiers 6 Understands several modifiers in a sentence (i.e., small green apple) Two Modifiers 7 Understands size (can select the largest/smallest object out of a collection of objects) Size Superlatives 8 Understands NUMBERS (i.e., two apples vs. three apples) Numbers Syntactic Mechanism 9 Understands spatial prepositions (i.e., put the apple ON TOP of the box vs. INSIDE the box vs. BEHIND the box) Sp. Prepositions 10 Understands verb tenses (i.e., I will eat an apple vs. I ate an apple) Verb Tenses 11 Understands simple stories that are read aloud Simple Stories 12 Understands elaborate fairytales that are read aloud (i.e., stories describing FANTASY creatures) Elab. Fairytales 13 Understands possessive pronouns (i.e., your apple vs. her apple) Poss. Pronouns 14 Understands the change in meaning when the order of words is changed (i.e., understands the difference between 'a cat ate a mouse' vs. 'a mouse ate a cat') Flexible Syntax 15 Understands explanations about people, objects or situations beyond the immediate surroundings (e.g., “Mom is walking the dog,” “The snow has turned to water”). Explanations Table 2 Eleven expressive language items as they were posed to parents. Response options were: very true (0 points), somewhat true (1 point), and not true (2 points). All items were a part of the expressive language ATEC subscale 1. A lower score indicates superior expressive ability. Expressive language items (verbatim) Abbreviations used in dendrograms 1 [My child] Can use one word at a time (No!, Eat, Water, etc.) Uses 1 word 2 Knows 10 or more words Knows 10 + words 2 Can use 2 words at a time (Don't want, Go home) Uses 2 words 3 Can use 3 words at a time (Want more milk) Uses 3 words 5 Can use sentences with 4 or more words Uses 4 + words 6 Explains what he/she wants Explains what wants 7 Speech tends to be meaningful/relevant Speech meaningful 8 Carries on fairly good conversation Good conversation 9 Has normal ability to communicate for his/her age Normal communic. 10 Asks meaningful questions Asks questions 11 Often uses several successive sentences Uses several sentences The inclusion criteria for this study remained consistent with those of previous studies 45 – 47 : absence of seizures (which commonly result in intermittent, unstable language deficits 48 ), absence of serious and moderate sleep problems (which are also associated with intermittent, unstable language deficits 49 ), age range of 4 to 22 years. The lower age cutoff was chosen to ensure that participants were exposed to a complete set of items listed in Table 1 50 , while the upper age cutoff reflects the limited availability of older participants in the cohort. When caregivers completed several evaluations, the last evaluation was used for analysis. Autism level (mild/Level 1, moderate/Level 2, or severe/Level 3) was reported by caregivers (Table 3 ). Pervasive Developmental Disorder and Asperger Syndrome were combined with mild autism for analysis as recommended by DSM-5 1 . A good reliability of such parent-reported diagnosis has been previously demonstrated 51 . Thus, the study included a total of 62,414 participants, the average age was 6.7 ± 2.4 years (range of 4 to 22 years), 78.5% participants were males (Table 3 ). English was spoken by 37.5% of participants, Spanish by 30.8%, Portuguese by 10%, Italian by 8.3%, Russian by 5.4%, Chinese by 3%, German by 1.1%, French by 1.1%, Korean by 0.5%, other by 2.3% The education level of participants’ parents was the following: 90.9% with at least a high school diploma, 68.6% with at least college education, 35.8% with at least a master’s, and 5.6% with a doctorate. Table 3 Participants’ diagnoses as reported by caregivers. Number of Participants Percent of Total Age, Mean(SD) Percent Males Mild ASD 31072 49.8 6.3(2.4) 76.8 Moderate ASD 19844 31.8 6.9(2.8) 80.0 Severe ASD 11498 18.4 7.6(3.3) 80.6 Total 62414 100.0 6.7(2.4) 78.5 The study was conducted in accordance with the Declaration of Helsinki 52 . Informed consent was obtained from the caregivers of all participants. The study protocol was approved by the Biomedical Research Alliance of New York (BRANY) LLC Institutional Review Board (BRANY IRB File # 22-12-205-1120). Statistics and Reproducibility All fifteen available language comprehension items (Table 1 ) and eleven expressive language items (Table 2 ) from the 133-item questionnaire were included in the cluster analysis. Unsupervised Hierarchical Cluster Analysis (UHCA) was performed using Ward’s agglomeration method with a Euclidean distance metric. The clustering analysis was data-driven without any design or hypothesis. A two-dimensional heatmap was generated using the “pheatmap” package of R, freely available language for statistical computing 53 . Code and data can be downloaded from https://doi.org/10.17605/OSF.IO/2QK5B . Results Clustering analysis of language comprehension abilities Caregivers assessed 15 language comprehension abilities (Table 1 ). To examine patterns of co-occurrence among these abilities, we applied unsupervised hierarchical cluster analysis (UHCA)—a data-driven method that groups items based on their similarity. This technique produces tree-like diagrams, called dendrograms, which visually represent the hierarchical relationships between clusters of items. Abilities that frequently co-occur are automatically positioned closer together, while those that co-occur less often appear farther apart. The analysis shown in Fig. 1 A identified the same three clusters previously reported in our earlier studies 45 – 47 . The distances between these three clusters (measured by Height) were significantly larger than the distances between subclusters, indicating clear separation. The first cluster included knowing the name, responding to ‘No’ or ‘Stop’, responding to praise , and following some commands (items 1 to 4 in Table 1 ) and was termed the Command Mechanism. The second cluster included understanding color and size modifiers, several modifiers in a sentence, size superlatives , and numbers (items 5 to 8 in Table 1 ) and was termed the Modifier Mechanism. The third cluster included understanding of spatial prepositions, verb tenses, flexible syntax, possessive pronouns, explanations about people and situations, simple stories , and elaborate fairytales (items 9 to 15 in Table 1 ) and was termed the Syntactic Mechanism. Our previous analysis demonstrated that these clusters were stable across different evaluation methods, age groups, time points, genders, spoken-languages, and levels of parental education 45 – 47 . Principal component analysis (PCA; Fig. 1 B) further confirmed a clear separation among the Command, Modifier, and Syntactic clusters. As a control we calculated UHCA and PCA of the 15 language comprehension abilities along with the hyperactivity (Supplementary Fig. 1), bed-wetting (Supplementary Fig. 2), and demands sameness (Supplementary Fig. 3) items. Since these items are not related to language, they would be expected to form a separate cluster. As anticipated, both UHCA and PCA grouped these items into their own cluster at a significant distance from the three language clusters, indicating random co-occurrence with the language abilities, and, thus, validating the effectiveness of both clustering techniques. Clustering analysis of expressive language abilities Caregivers evaluated 11 expressive language abilities (Table 2 ). Figure 2 A presents the dendrogram produced by UHCA, where the height of the branches indicates the distance between clusters; greater distance corresponds to lower co-occurrence between the abilities. Three clusters have inter-cluster distances that are larger than the distances between subclusters. The first cluster, termed Single-Word items, includes: [My child] can use one word at a time and knows 10 or more words (Table 2 , items 1 and 2). The second cluster, termed Single-Sentence, includes: [My child] can use 2 words at a time, can use three words at a time, can use sentences with 4 or more words, explains what they want , and speech tends to be meaningful/relevant (items 3 to 7). The third cluster, termed the Multi-Sentence, includes: [My child] carries on fairly good conversation, has normal ability to communicate for their age, asks meaningful questions , and often uses several successive sentences (items 8 to 11). Another possible interpretation of the dendrogram is a two-cluster solution (Fig. 2 A: Single-Word items and Single-Sentence items in one cluster and Multi-Sentence items in another cluster). However, some clustering methods merge the Multi-Sentence cluster with the Single-Sentence cluster, making the two-cluster solution unstable (see the “Average” clustering method, Supplementary Fig. 4C, and the “Mcquitty” clustering method, Supplementary Fig. 4E). Furthermore, in the PCA plot, the three clusters—Single-Word, Single-Sentence, and Multi-Sentence—do not overlap (Fig. 2 B). We have also considered a four-cluster solution based on Fig. 2 A, where [My child] can use two words at a time and can use three words at a time items form a fourth cluster. However, the three-cluster solution is superior to the four-cluster solution because 1) it has greater average inter-cluster distance on the dendrogram; 2) PCA does not show separation between [My child] can use two words at a time and can use three words at a time items from the Single-Sentence cluster (Fig. 2 B); and 3) some clustering methods combine the fourth cluster with the [My child] can use sentences with 4 words or more words item demonstrating that the fourth cluster is unstable (see the “Average” clustering method, Supplementary Fig. 4C, and the “Mcquitty” clustering method, Supplementary Fig. 4E). Consequently, the three-cluster solution is superior to both two- and four-cluster solutions based on the average inter-cluster distance of UHCA, stability, and also PCA. The three-cluster solution was stable across multiple seeds as well as consistent across different evaluation methods (Ward.D2 method, Fig. 2 A; Ward.D method, Supplementary Fig. 4D; Average method, Supplementary Fig. 4C; Complete method, Supplementary Fig. 4D; Mcquitty method, Supplementary Fig. 4E), and across different time points (first evaluation, Supplementary Fig. 5; last evaluation, Fig. 2 ). As a control we calculated UHCA and PCA of the 11 expressive language abilities along with the hyperactivity (Supplementary Fig. 6), bed-wetting (Supplementary Fig. 7), and demands sameness (Supplementary Fig. 8) items. These items are not related to speech and were therefore expected to form a separate cluster. As anticipated, both UHCA and PCA grouped these items into their own cluster at a significant distance from the three expressive clusters, validating the effectiveness of both clustering techniques. Clustering analysis of language comprehension abilities together with expressive language abilities Figure 3 A depicts the UHCA dendrogram of the 15 language comprehension items (Table 1 ) together with the 11 expressive language items (Table 2 ). The UHCA can be interpreted as either three separate clusters or as six distinct clusters. The PCA of the same items (Fig. 3 B) clarifies the existence of six clusters of items. These clusters are identical to those detected by separate clustering of receptive and expressive items: three language comprehension clusters (Command, Modifier, and Syntactic) and three expressive language clusters (Single-Word, Single-Sentence, and Multi-Sentence). This six-cluster solution was stable across multiple seeds and across different time points (first evaluation, Supplementary Fig. 9; last evaluation, Fig. 3 ). Language comprehension phenotypes in participants The principles underlying participant clustering are the same as those used for clustering abilities: individuals with similar patterns of abilities are automatically grouped into hierarchical dendrograms, revealing distinct participant phenotypes. A two-dimensional heatmap (Fig. 4 ) enables the visualization of the relationship between participant phenotypes and language comprehension abilities. The 62,414 participants are shown as columns; the participants’ dendrogram is shown at the top. The 15 comprehension abilities are shown as rows; the abilities’ dendrogram is shown on the left (it is the same dendrogram as shown in Fig. 1 A). Blue indicates the presence of a linguistic ability (parent’s response = very true ); red indicates the lack of a linguistic ability (parent’s response = not true ); and yellow indicates an intermittent presence of a linguistic ability (parent’s response = somewhat true ). The three clusters of participants match the three language comprehension mechanisms. The middle cluster of participants (marked Syntactic Phenotype) shows the predominant blue color (representing good skills) across all three language comprehension mechanisms, indicating that these participants have acquired the Command, Modifier, and Syntactic Mechanisms (17.4% of participants, Table 4 ). Table 4 Comprehension Phenotypes. Participant cluster statistics. Syntactic-comprehension Phenotype Modifier-comprehension Phenotype Command-comprehension Phenotype Total Number of participants 10882 29328 22204 62414 Percent of Total 17.4 47.0 35.6 100 Age, Mean(SD) 6.6(2.5) 6.6(2.7) 6.8(3.0) 6.7(2.8) Percent Male 71.7 75.7 77.3 75.6 The leftmost cluster of participants (marked Command Phenotype) shows the predominant blue color only among the Command Mechanism items and red colors across the Syntactic and Modifier Mechanisms items, indicating that these individuals have acquired only the Command Mechanism (35.6%). The rightmost cluster of participants (marked Modifier Phenotype) shows the predominant blue color only across the Command and Modifier Mechanisms items and yellow to red colors across the Syntactic Mechanism items, indicating that these individuals have acquired only the Command and Modifier Mechanisms (47%). The three language comprehension phenotypes—Command, Modifier, and Syntactic—are congruent to those found in previous studies 45 – 47 . Table 5 shows phenotype assignment of participants by their diagnosis. As expected, the most-advanced Syntactic Phenotype contained more participants diagnosed with mild ASD (74.9% mild ASD vs. 5.3% severe ASD). Conversely, the Command Phenotype contained a comparable number of participants diagnosed with mild and severe ASD (33.6% mild ASD vs. 30.4% severe ASD). Table 5 ASD severity distribution (%) within the three language comprehension phenotypes. Syntactic-comprehension Phenotype Modifier-comprehension Phenotype Command-comprehension Phenotype Mild ASD 74.9 52.7 33.6 Moderate ASD 19.7 33.1 35.9 Severe ASD 5.3 14.2 30.4 Total 100 100 100 Expressive language phenotypes in participants A two-dimensional heatmap relating participants to their expressive language abilities reveals four expressive language phenotypes (Fig. 5 , the dendrogram is displayed horizontally, at the top). The four clusters of participants match the three clusters of linguistic abilities (the abilities’ dendrogram is shown on the left; it is the same dendrogram as shown in Fig. 2 A). The leftmost cluster of participants shows the predominant blue color (indicating competent skills) across all three clusters of expressive abilities and is therefore termed the Multi-Sentence Phenotype (13.2% of participants, Table 6 ). The next cluster of participants shows the predominant blue color among Single-Word items and Single-Sentence items and is therefore termed the Single-Sentence Phenotype (24.1%). The third cluster of participants shows the predominant red colors across all expressive abilities and is therefore termed the Nonverbal Phenotype (28.4%). The right-most cluster of participants shows the predominant blue color only across Single-Word items and is therefore termed the Single-Word Phenotype (34.4%). Table 6 Participant statistics in the four expressive language clusters. Multi-Sentence-Expression Phenotype Single-Sentence-Expression Phenotype Single-Word-Expression Phenotype Nonverbal Phenotype Total Number of participants 8208 15049 21441 17716 62414 Percent of Total 13.2 24.1 34.4 28.4 100 Age, Mean(SD) 6.5 (2.4) 6.8 (2.6) 6.7 (2.9) 6.8 (3.0) 6.7 (2.8) Percent Male 67.5 76.4 75.7 78.6 75.6 Table 7 shows cluster assignment of participants by their diagnosis. As expected, the most-advanced Muti-Sentence Phenotype contained more participants diagnosed with mild ASD (78.8% mild ASD vs. 5% severe ASD). Conversely, the Nonverbal Phenotype contained more participants diagnosed with severe ASD (29.7% mild ASD vs. 34.4% severe ASD). Table 7 ASD severity distribution (%) within the four expressive language phenotypes. Multi-Sentence-Expression Phenotype Single-Sentence-Expression Phenotype Single-Word-Expression Phenotype Nonverbal Phenotype Mild ASD 78.8 61.5 47.0 29.7 Moderate ASD 16.2 30.1 35.5 35.9 Severe ASD 5.0 8.3 17.5 34.4 Total 100 100 100 100 Combined receptive-expressive language phenotypes in participants A two-dimensional heatmap relating participants to linguistic abilities reveals four distinct receptive-expressive language phenotypes (Fig. 6 , horizontally, at the top). The leftmost cluster of participants shows the predominant blue color (indicating competency) across all six clusters of linguistic items and is therefore termed the Multi-Sentence-Expression with Syntactic-comprehension Phenotype (marked on Fig. 6 as “Multi-Sentence + Syntactic;” 9.7% of participants, Table 8 ). Table 8 Participant cluster statistics in the four receptive-expressive phenotypes. Multi-Sentence-Expression with Syntactic-comprehension Phenotype Single-Sentence-Expression with Modifier-comprehension Phenotype Single-Word- Expression with Modifier-comprehension Phenotype Nonverbal with Command-comprehension Phenotype Total Number of participants 6041 15863 21481 19029 62414 Percent of Total 9.7 25.4 34.4 30.5 100 Age, Mean (SD) 6.5 (2.3) 6.6 (2.6) 6.7 (2.8) 6.8 (3.0) 6.7 (2.8) Percent Male 68.9 76.3 74.3 78.6 75.5 The next cluster of participants shows the predominant blue color across Command- and Modifier-comprehension items as well as among the Single-Word- and Single-Sentence-Expression items. This cluster was therefore termed the Single-Sentence-Expression with Modifier-comprehension Phenotype (Single-Sentence + Modifier; 25.4%). The third cluster of participants shows red color as predominant across all the receptive and expressive items, except the Command-comprehension items where this cluster shows the predominant blue color. Consequently, it is termed the Nonverbal with Command-comprehension Phenotype (Nonverbal + Command; 30.5%). The rightmost cluster of participants shows the predominant blue color across Command- and Modifier-comprehension items, and Single-Word-Expression items. This cluster was therefore termed the Single-Word-Expression Phenotype with Modifier-comprehension (Single-Word + Modifier; 22.3%). Table 9 shows cluster assignment of participants by their diagnosis. As expected, the most-advanced Multi-Sentence-Expression with Syntactic-comprehension Phenotype contained more participants diagnosed with mild ASD (80.4% mild ASD vs. 4.0% severe ASD). Conversely, the Nonverbal with Command-comprehension Phenotype contained more participants diagnosed with severe ASD (29.4% mild ASD vs. 34.3% severe ASD). Table 9 ASD severity distribution (%) within the four receptive-expressive phenotypes. Multi-Sentence-Expression with Syntactic-comprehension Phenotype Single-Sentence-Expression with Modifier-comprehension Phenotype Single-Word- Expression with Modifier-comprehension Phenotype Nonverbal with Command-comprehension Phenotype Mild ASD 80.4 64.6 48.3 29.4 Moderate ASD 15.6 28.2 35.0 36.3 Severe ASD 4.0 7.1 16.7 34.3 Total 100 100 100 100 Discussion Receptive-expressive phenotypes Clustering analysis of 62,414 autistic participants based on both receptive and expressive items identified four phenotypes (Fig. 6 ): (1) Multi-Sentence with Syntactic- comprehension; (2) Single-Sentence with Modifier-comprehension ; (3) Single-Word with Modifier-comprehension ; and (4) Nonverbal with Command-comprehension . Within the tripartite framework, phenotype (1) corresponds to Verbal Individuals without Structural Language Impairment; phenotype (2) corresponds to Verbal Individuals with Structural Language Impairment, and phenotypes (3) and (4) correspond to Minimally Verbal Individuals. These four phenotypes identified in this study closely parallel the four latent classes reported by Munson et al. 26 in their analysis of 456 autistic children using Mullen’s Expressive Language, Receptive Language, Visual Reception, and Fine Movement scales. However, comparisons between the two studies must take into account important differences in measurement. Although the Mullen Expressive Language scale is conceptually similar to the expressive language measure used here, the Mullen Receptive Language scale differs substantially from the syntactic language comprehension scale employed in this study (MSEC 43 , 54 ). The MSEC emphasizes combinatorial language processing—that is, the ability to derive meaning from word order and syntactic relations (e.g., “a cat ate a mouse” vs. “a mouse ate a cat”). In contrast, the Mullen Receptive Language scale primarily assesses functional comprehension of individual words, labels, and categories, with relatively few items probing combinatorial or syntactic processing. Consequently, the construct captured by the MSEC aligns more closely with the Mullen Receptive Language scale only when considered jointly with the Mullen Visual Reception scale, which indexes nonverbal reasoning demands relevant to syntactic interpretation. Under this alignment, the phenotypes identified in the present study correspond to those of Munson et al. 26 as follows: 1) Multi-Sentence-Expression with Syntactic-comprehension Phenotype corresponds to the High Verbal / High Nonverbal group, characterized by mean Mullen Expressive, Receptive, and Visual Reception IQs of approximately 87, 90, and 110, respectively. 2) Single-Sentence-Expression with Modifier-comprehension corresponds to the Medium Verbal / Medium Nonverbal group, with mean Expressive, Receptive, and Visual IQs of approximately 62, 64, and 70. 3) Single-Word-Expression with Modifier-comprehension corresponds to the Low Verbal / Medium Nonverbal group, with mean Expressive, Receptive, and Visual IQs of approximately 40, 35, and 75. 4) Nonverbal with Command-comprehension corresponds to the Low Verbal / Low Nonverbal group, characterized by mean Mullen Expressive Language, Receptive Language, and Visual IQs of approximately 30, 27, and 50. The convergence between the receptive–expressive phenotypes identified in this study and those reported by Munson et al. 26 suggest that the traditional tripartite framework oversimplifies the classification of autistic language phenotypes by collapsing the Single-Word with Modifier-comprehension phenotype together with the Nonverbal with Command-comprehension phenotype (or, in Munson et al.’s terminology, the Low Verbal / Medium Nonverbal and Low Verbal / Low Nonverbal group). As shown in Fig. 6 , these two profiles are clearly distinct in both expressive and receptive dimensions. Moreover, even these four broad phenotypes fail to capture less common but theoretically informative profiles, such as the Nonverbal with Syntactic-comprehension Phenotype , which includes mute individuals who demonstrate intact syntactic comprehension. These observations indicate that one-dimensional categorical frameworks are insufficient to represent the full heterogeneity of language abilities in autism. A more accurate and conceptually grounded classification system would therefore move beyond broad categorical groupings and instead adopt a two-dimensional framework, in which expressive ability and language comprehension are treated as independent dimensions. Such an approach would naturally accommodate both common and rare profiles, avoid forced category merging, and more accurately reflect the continuous and multidimensional nature of language abilities in autism. Although this two-dimensional framework is widely used in the field of developmental language disorders, it remains relatively uncommon in autism research. This study’s analysis confirmed three previously identified 45 – 47 comprehension phenotypes: Command, Modifier, and Syntactic (Fig. 4 ). Individuals with the Command Phenotype were limited to the understanding of single words and simple commands. Those with the Modifier Phenotype additionally demonstrated the ability to integrate nouns with modifiers such as color, size, and number. Participants with the Syntactic Phenotype additionally showed comprehension of spatial prepositions, verb tenses, flexible syntax, possessive pronouns, and complex narratives such as stories and fairy tales. For comparison, typically-developing children acquire the Command Phenotype around 2 years of age, the Modifier Phenotype around 3 years, and the Syntactic Phenotype around 4 years 55 . Separately, clustering analysis of expressive items identified four expressive phenotypes (Fig. 5 ). Participants in the first group had not acquired functional expressive language (Nonverbal Phenotype). The second group primarily used isolated words (Single-Word Phenotype). The third group produced single sentences (Single-Sentence Phenotype), whereas the fourth group exhibited multi-sentence expressive language (Multi-Sentence Phenotype). In typically-developing children, the Single-Word Phenotype may emerge as early as 1.5 years, the Single-Sentence Phenotype by approximately 2 years, and the Multi-Sentence Phenotype by around 3 years of age 56 . Limitations Epidemiological studies of app users provide access to a large number of individuals, but have obvious downsides, such as relying on parent reports. On one hand, parents may yield to wishful thinking and overestimate their children's abilities 57 ; on the other, parents possess a deep understanding of their children. This understanding may be especially important for language comprehension assessment, the evaluation of which in a clinical office can be very tricky. Multiple previous studies suggest that parent reports of language skills do not significantly differ from direct clinicians’ assessments 55 , 58 – 60 . Additionally, studies of our database also indicate the prevalence of consistent and truthful parent reports 27 , 49 , 51 . Otherwise, if parents were overestimating their children’s linguistic abilities, we would see a much larger proportion of children in most-advanced receptive/expressive phenotypes. The fact that most children landed in most-basic phenotypes speaks in favor of realistic estimates of children’s abilities. Clinical implications This study identified three receptive language phenotypes—Command, Modifier, and Syntactic—and four expressive phenotypes—Nonverbal, Single-Word, Single-Sentence, and Multi-Sentence—in autistic individuals. These findings indicate that the traditional tripartite classification does not fully capture the diversity of language profiles observed in clinical practice. In contrast, a two-dimensional classification system that jointly considers receptive and expressive language abilities provides a more accurate and nuanced characterization of communicative functioning. Adopting such a framework would allow clinicians to distinguish individuals who share similar expressive levels but differ substantially in comprehension, or vice versa—differences that are highly relevant for assessment, prognosis, and intervention planning. By explicitly representing domain-specific strengths and weaknesses, a two-dimensional approach could guide the selection of targeted, individualized language interventions and support more precise goal-setting. Ultimately, this refined classification has the potential to facilitate more personalized therapeutic strategies and improve functional communication outcomes for autistic individuals. Declarations Funding This research received no external funding. Acknowledgements We wish to thank all participants’ caregivers who found time to complete children’s assessments. The language therapy app used to collect the data presented in this manuscript was made possible by the contributions of Rita Dunn, Alexander Faisman, Jonah Elgart, Lisa Lokshina, and Yulia Dumov. Author contributions AV and EK designed the study. KG, RV, EK, and AV analyzed the data. KG and AV wrote the paper. Competing Interests Authors declare no competing interests. References American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®) . (American Psychiatric Pub, 2013). Kozlowski, A. M., Matson, J. L., Horovitz, M., Worley, J. A. & Neal, D. Parents’ first concerns of their child’s development in toddlers with autism spectrum disorders. Dev. Neurorehabilitation 14 , 72–78 (2011). Chow, J. C., Broda, M. D., Granger, K. L., Deering, B. T. & Dunn, K. T. 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(A) The dendrogram representing the hierarchical clustering of language comprehension abilities. (B) Principal component analysis of language comprehension abilities shows clear separation between Command, Modifier, and Syntactic items. Principal component 1 accounts for 43.1% of the variance in the data. Principal component 2 accounts for 11.3% of the variance in the data.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/1d2af5d277cc948abece7961.png"},{"id":100726450,"identity":"769c3fee-be01-4bff-b4ea-276f7d5c596c","added_by":"auto","created_at":"2026-01-20 20:25:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClustering analysis of 11 expressive language items. (A) The dendrogram representing the unsupervised hierarchical clustering analysis of expressive language abilities. (B) Principal component analysis of expressive language abilities shows a clear separation between Single-Word, Single-Sentence, and Multi-Sentence items. Principal component 1 accounts for 43.1% of the variance in the data. Principal component 2 accounts for 11.3% of the variance in the data.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/e1811b4a3b7e645ddd991ae6.png"},{"id":100726457,"identity":"3f93fe25-4f0c-4fb4-b476-19978d63b7c1","added_by":"auto","created_at":"2026-01-20 20:25:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":406177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClustering analysis of 15 receptive language and 11 expressive language items. (A) The dendrogram represents the UHCA of 26 items. (B) PCA of the 26 items shows a clear separation between the six clusters: three language comprehension clusters on the bottom (Command, Modifier, and Syntactic) and three expressive clusters on the top(Single-Word, Single-Sentence, and Multi-Sentence). Principal component 1 accounts for 38.8% of the variance in the data. Principal component 2 accounts for 10.7% of the variance in the data.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/434ecdb7360e898f807c27fa.png"},{"id":100726123,"identity":"0494d3a8-e85f-434c-9d77-c9ac2cc98c95","added_by":"auto","created_at":"2026-01-20 20:21:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":410518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-dimensional heatmap relating participants to their language comprehension abilities. The 15 language comprehension abilities are shown as rows. The dendrogram representing language comprehension abilities is shown on the left. Participants are shown as 62,414 columns. The dendrogram representing participants is shown horizontally at the top. Blue color indicates the presence of a linguistic ability (the “very true” answer), red indicates the lack of a linguistic ability (the “not true” answer), and yellow indicates the “somewhat true” answer.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/a0dfdb66691586b00cf4bcc7.png"},{"id":100726493,"identity":"0190a69c-a5ef-44c7-b99a-e29c20a138e5","added_by":"auto","created_at":"2026-01-20 20:26:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":324969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-dimensional heatmap relating participants to their expressive language abilities. The 11 expressive abilities are shown as rows. The dendrogram representing expressive language items is shown on the left. Participants are shown as 62,414 columns. The dendrogram representing participants is shown horizontally at the top. Blue color indicates the presence of a linguistic ability (the “very true” answer), red indicates the lack of a linguistic ability (the “not true” answer), and yellow indicates the “somewhat true” answer.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/8651154cba7d913a5db35570.png"},{"id":100725839,"identity":"59be410f-701f-44db-a7ce-1e3dd48254ac","added_by":"auto","created_at":"2026-01-20 20:17:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":463377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-dimensional heatmap relating participants to their receptive and expressive language abilities. The 15 receptive language items and 11 expressive language items are shown as rows. The dendrogram representing receptive-expressive language abilities shows six clusters of items (vertically on the left); the three receptive language clusters are marked: Command, Modifier, and Syntactic; the three expressive language clusters are marked: Single-Word, Single-Sentence, and Multi-Sentence. Participants are shown as 62,414 columns. The dendrogram of participants clustering is shown horizontally at the top. Blue color indicates the presence of a linguistic ability (the “very true” answer), red indicates the lack of a linguistic ability (the “not true” answer), and yellow indicates the “somewhat true” answer. The four participant phenotypes are marked (from left to right): 1) Multi-Sentence + Syntactic; 2) Single-Sentence + Modifier; 3) Nonverbal + Command; 4) Single-Word + Modifier.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/fc096c313bbe6ed6feb7c699.png"},{"id":101749785,"identity":"08fadba4-7a13-4c8d-9f52-abde49be01a3","added_by":"auto","created_at":"2026-02-03 09:58:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5192226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/9c7072f1-463c-466e-bb60-342404d3631f.pdf"},{"id":100726698,"identity":"71b4061b-ccdb-4049-accd-726ddc6c800f","added_by":"auto","created_at":"2026-01-20 20:28:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2620838,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial20250806.docx","url":"https://assets-eu.researchsquare.com/files/rs-8466583/v1/3e6617f17ee450b2e50a36d7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Receptive and expressive language phenotyping in over 62,000 autistic Individuals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and the presence of restricted, repetitive patterns of behavior \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Across most countries, delayed language and communication development relative to peers is the primary reason parents seek formal clinical evaluation and diagnosis \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Language abilities are among the most robust and stable predictors of later social \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and educational \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e functioning. Converging evidence from multiple studies suggests that language deficits may constitute a core feature of ASD \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and language therapy remains among the most widely implemented interventions \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Accordingly, refining the classification of language phenotypes in ASD is an important goal for optimizing intervention strategies and improving developmental outcomes \u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConventional descriptions of communication levels in ASD distinguish three broad groups: 1) verbal individuals without structural language impairment (sound structure and grammar), 2) verbal individuals with structural language impairment, and 3) minimally verbal autistic individuals \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, this tripartite framework likely oversimplifies the substantial heterogeneity in language abilities observed across autistic individuals. First, despite its widespread use, the tripartite classification has not been validated in large cohorts, leaving it unclear whether it adequately captures even broad categories of language ability. For example, Munson et al. \u003csup\u003e26\u003c/sup\u003e identified four latent phenotypes in an analysis of 456 autistic children: 1) Low Verbal / Low Nonverbal; 2) Low Verbal / Medium Nonverbal; 3) Medium Verbal / Medium Nonverbal, and 4) High Verbal / High Nonverbal. Although that study operationalized \u003cem\u003everbal\u003c/em\u003e ability as the mean of receptive and expressive language Mullen scales, and \u003cem\u003enonverbal\u003c/em\u003e ability as the mean of visual reception and fine motor scales, its findings nonetheless point to a more complex structure than the traditional three-group model.\u003c/p\u003e \u003cp\u003eSecond, numerous studies have demonstrated that receptive and expressive language abilities do not always align \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and that in many autistic individuals receptive language is more impaired than expressive language \u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34 CR35\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These findings indicate that separately characterizing receptive and expressive language phenotypes may yield a more informative and accurate classification system.\u003c/p\u003e \u003cp\u003eFor over a decade, our group has collected longitudinal, parent-reported data on language, social functioning, and health in autistic children. This resource now comprises data from more than 100,000 autistic individuals, offering a unique opportunity to address unresolved questions regarding language phenotypes at scale.\u003c/p\u003e \u003cp\u003eAccordingly, this study had three primary aims: (1) to characterize receptive language phenotypes; (2) to independently identify expressive language phenotypes; and (3) to evaluate whether broad receptive\u0026ndash;expressive language groupings can be validated in a large cohort of autistic individuals. Replication of three broad groupings aligned with the traditional tripartite framework would provide empirical support for that model. In contrast, failure to identify such groupings would suggest that a two-dimensional framework, independently assessing receptive and expressive language phenotypes, offers greater precision.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eParticipants were children and adolescents using a language therapy app that was made freely available at all major app stores in September 2015 \u003csup\u003e37\u0026ndash;41\u003c/sup\u003e. The app provides various structured language comprehension therapy exercises and is primarily used by caregivers of children with language impairments. Most of the caregivers are presumed to be parents. Once the app was downloaded, caregivers were asked to register and to provide demographic details, including the child\u0026rsquo;s diagnosis and age. Caregivers completed a 133-item questionnaire (77-item Autism Treatment Evaluation Checklist (ATEC) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (Supplementary Tables\u0026nbsp;1\u0026ndash;4); 20-item Mental Synthesis Evaluation Checklist (MSEC) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;5); 10-item screen time checklist \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; 25-item diet checklist \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e; and 1-item parent education survey) approximately every three months.\u003c/p\u003e \u003cp\u003eAll fifteen available \u003cem\u003elanguage comprehension\u003c/em\u003e items from the 133-item questionnaire were included in the cluster analysis as in previously published articles \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, all 11 available expressive items were included in the cluster analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Response options were: very true (0 points), somewhat true (1 point), and not true (2 points).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eThree language comprehension mechanisms\u0026mdash;Syntactic, Modifier, and Command\u0026mdash;have been identified in previous studies\u003c/b\u003e \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The language comprehension items are presented exactly as surveyed with parents in both this and earlier studies. Response options were: very true, somewhat true, and not true. Items 1 to 3 were assessed as part of the Expressive Language ATEC \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e subscale 1; the rest of the items were part of the MSEC subscale \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLanguage comprehension items (verbatim)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbbreviations used in dendrograms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCommand Mechanism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnows own name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKnows Name\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponds to \u0026lsquo;No\u0026rsquo; or \u0026lsquo;Stop\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo and Stop\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponds to praise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResp. to Praise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCan follow some commands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommands\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eModifier Mechanism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands some simple modifiers (i.e., green apple vs. red apple or big apple vs. small apple)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eColor or Size / Modifiers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands several modifiers in a sentence (i.e., small green apple)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTwo Modifiers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands size (can select the largest/smallest object out of a collection of objects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSize Superlatives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands NUMBERS (i.e., two apples vs. three apples)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumbers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eSyntactic Mechanism \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands spatial prepositions (i.e., put the apple ON TOP of the box vs. INSIDE the box vs. BEHIND the box)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSp. Prepositions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands verb tenses (i.e., I will eat an apple vs. I ate an apple)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVerb Tenses\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands simple stories that are read aloud\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple Stories\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands elaborate fairytales that are read aloud (i.e., stories describing FANTASY creatures)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElab. Fairytales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands possessive pronouns (i.e., your apple vs. her apple)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoss. Pronouns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands the change in meaning when the order of words is changed (i.e., understands the difference between 'a cat ate a mouse' vs. 'a mouse ate a cat')\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlexible Syntax\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstands explanations about people, objects or situations beyond the immediate surroundings (e.g., \u0026ldquo;Mom is walking the dog,\u0026rdquo; \u0026ldquo;The snow has turned to water\u0026rdquo;).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExplanations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eEleven expressive language items as they were posed to parents.\u003c/b\u003e Response options were: very true (0 points), somewhat true (1 point), and not true (2 points). All items were a part of the expressive language ATEC subscale 1. A lower score indicates superior expressive ability.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpressive language items (verbatim)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviations used in dendrograms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[My child] Can use one word at a time (No!, Eat, Water, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses 1 word\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnows 10 or more words\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnows 10\u0026thinsp;+\u0026thinsp;words\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan use 2 words at a time (Don't want, Go home)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses 2 words\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan use 3 words at a time (Want more milk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses 3 words\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan use sentences with 4 or more words\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses 4\u0026thinsp;+\u0026thinsp;words\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplains what he/she wants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExplains what wants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpeech tends to be meaningful/relevant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpeech meaningful\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarries on fairly good conversation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood conversation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHas normal ability to communicate for his/her age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal communic.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsks meaningful questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsks questions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOften uses several successive sentences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses several sentences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe inclusion criteria for this study remained consistent with those of previous studies \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e: absence of seizures (which commonly result in intermittent, unstable language deficits \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e), absence of serious and moderate sleep problems (which are also associated with intermittent, unstable language deficits \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e), age range of 4 to 22 years. The lower age cutoff was chosen to ensure that participants were exposed to a complete set of items listed in Table\u0026nbsp;1 \u003csup\u003e50\u003c/sup\u003e, while the upper age cutoff reflects the limited availability of older participants in the cohort.\u003c/p\u003e \u003cp\u003eWhen caregivers completed several evaluations, the last evaluation was used for analysis. Autism level (mild/Level 1, moderate/Level 2, or severe/Level 3) was reported by caregivers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Pervasive Developmental Disorder and Asperger Syndrome were combined with mild autism for analysis as recommended by DSM-5 \u003csup\u003e1\u003c/sup\u003e. A good reliability of such parent-reported diagnosis has been previously demonstrated \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Thus, the study included a total of 62,414 participants, the average age was 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years (range of 4 to 22 years), 78.5% participants were males (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). English was spoken by 37.5% of participants, Spanish by 30.8%, Portuguese by 10%, Italian by 8.3%, Russian by 5.4%, Chinese by 3%, German by 1.1%, French by 1.1%, Korean by 0.5%, other by 2.3% The education level of participants\u0026rsquo; parents was the following: 90.9% with at least a high school diploma, 68.6% with at least college education, 35.8% with at least a master\u0026rsquo;s, and 5.6% with a doctorate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; diagnoses as reported by caregivers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge, Mean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercent Males\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMild ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.9(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSevere ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e62414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.7(2.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e78.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Informed consent was obtained from the caregivers of all participants. The study protocol was approved by the Biomedical Research Alliance of New York (BRANY) LLC Institutional Review Board (BRANY IRB File # 22-12-205-1120).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistics and Reproducibility\u003c/h3\u003e\n\u003cp\u003eAll fifteen available language comprehension items (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and eleven expressive language items (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) from the 133-item questionnaire were included in the cluster analysis. Unsupervised Hierarchical Cluster Analysis (UHCA) was performed using Ward\u0026rsquo;s agglomeration method with a Euclidean distance metric. The clustering analysis was data-driven without any design or hypothesis. A two-dimensional heatmap was generated using the \u0026ldquo;pheatmap\u0026rdquo; package of R, freely available language for statistical computing \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Code and data can be downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/2QK5B\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/2QK5B\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eClustering analysis of language comprehension abilities\u003c/h2\u003e \u003cp\u003eCaregivers assessed 15 language comprehension abilities (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To examine patterns of co-occurrence among these abilities, we applied unsupervised hierarchical cluster analysis (UHCA)\u0026mdash;a data-driven method that groups items based on their similarity. This technique produces tree-like diagrams, called dendrograms, which visually represent the hierarchical relationships between clusters of items. Abilities that frequently co-occur are automatically positioned closer together, while those that co-occur less often appear farther apart.\u003c/p\u003e \u003cp\u003eThe analysis shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA identified the same three clusters previously reported in our earlier studies \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The distances between these three clusters (measured by Height) were significantly larger than the distances between subclusters, indicating clear separation. The first cluster included \u003cem\u003eknowing the name, responding to \u0026lsquo;No\u0026rsquo; or \u0026lsquo;Stop\u0026rsquo;, responding to praise\u003c/em\u003e, and \u003cem\u003efollowing some commands\u003c/em\u003e (items 1 to 4 in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and was termed the Command Mechanism. The second cluster included \u003cem\u003eunderstanding color and size modifiers, several modifiers in a sentence, size superlatives\u003c/em\u003e, and \u003cem\u003enumbers\u003c/em\u003e (items 5 to 8 in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and was termed the Modifier Mechanism. The third cluster included \u003cem\u003eunderstanding of spatial prepositions, verb tenses, flexible syntax, possessive pronouns, explanations about people and situations, simple stories\u003c/em\u003e, and \u003cem\u003eelaborate fairytales\u003c/em\u003e (items 9 to 15 in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and was termed the Syntactic Mechanism. Our previous analysis demonstrated that these clusters were stable across different evaluation methods, age groups, time points, genders, spoken-languages, and levels of parental education \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Principal component analysis (PCA; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) further confirmed a clear separation among the Command, Modifier, and Syntactic clusters.\u003c/p\u003e \u003cp\u003eAs a control we calculated UHCA and PCA of the 15 language comprehension abilities along with the \u003cem\u003ehyperactivity\u003c/em\u003e (Supplementary Fig.\u0026nbsp;1), \u003cem\u003ebed-wetting\u003c/em\u003e (Supplementary Fig.\u0026nbsp;2), and \u003cem\u003edemands sameness\u003c/em\u003e (Supplementary Fig.\u0026nbsp;3) items. Since these items are not related to language, they would be expected to form a separate cluster. As anticipated, both UHCA and PCA grouped these items into their own cluster at a significant distance from the three language clusters, indicating random co-occurrence with the language abilities, and, thus, validating the effectiveness of both clustering techniques.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClustering analysis of expressive language abilities\u003c/h3\u003e\n\u003cp\u003eCaregivers evaluated 11 expressive language abilities (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA presents the dendrogram produced by UHCA, where the height of the branches indicates the distance between clusters; greater distance corresponds to lower co-occurrence between the abilities. Three clusters have inter-cluster distances that are larger than the distances between subclusters. The first cluster, termed Single-Word items, includes: \u003cem\u003e[My child] can use one word at a time\u003c/em\u003e and \u003cem\u003eknows 10 or more words\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, items 1 and 2). The second cluster, termed Single-Sentence, includes: \u003cem\u003e[My child] can use 2 words at a time, can use three words at a time, can use sentences with 4 or more words, explains what they want\u003c/em\u003e, and \u003cem\u003espeech tends to be meaningful/relevant\u003c/em\u003e (items 3 to 7). The third cluster, termed the Multi-Sentence, includes: \u003cem\u003e[My child] carries on fairly good conversation, has normal ability to communicate for their age, asks meaningful questions\u003c/em\u003e, and \u003cem\u003eoften uses several successive sentences\u003c/em\u003e (items 8 to 11).\u003c/p\u003e \u003cp\u003eAnother possible interpretation of the dendrogram is a two-cluster solution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA: Single-Word items and Single-Sentence items in one cluster and Multi-Sentence items in another cluster). However, some clustering methods merge the Multi-Sentence cluster with the Single-Sentence cluster, making the two-cluster solution unstable (see the \u0026ldquo;Average\u0026rdquo; clustering method, Supplementary Fig.\u0026nbsp;4C, and the \u0026ldquo;Mcquitty\u0026rdquo; clustering method, Supplementary Fig.\u0026nbsp;4E). Furthermore, in the PCA plot, the three clusters\u0026mdash;Single-Word, Single-Sentence, and Multi-Sentence\u0026mdash;do not overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eWe have also considered a four-cluster solution based on Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, where \u003cem\u003e[My child] can use two words at a time\u003c/em\u003e and \u003cem\u003ecan use three words at a time\u003c/em\u003e items form a fourth cluster. However, the three-cluster solution is superior to the four-cluster solution because 1) it has greater average inter-cluster distance on the dendrogram; 2) PCA does not show separation between \u003cem\u003e[My child] can use two words at a time\u003c/em\u003e and \u003cem\u003ecan use three words at a time\u003c/em\u003e items from the Single-Sentence cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB); and 3) some clustering methods combine the fourth cluster with the \u003cem\u003e[My child] can use sentences with 4 words or more words\u003c/em\u003e item demonstrating that the fourth cluster is unstable (see the \u0026ldquo;Average\u0026rdquo; clustering method, Supplementary Fig.\u0026nbsp;4C, and the \u0026ldquo;Mcquitty\u0026rdquo; clustering method, Supplementary Fig.\u0026nbsp;4E). Consequently, the three-cluster solution is superior to both two- and four-cluster solutions based on the average inter-cluster distance of UHCA, stability, and also PCA.\u003c/p\u003e \u003cp\u003eThe three-cluster solution was stable across multiple seeds as well as consistent across different evaluation methods (Ward.D2 method, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Ward.D method, Supplementary Fig.\u0026nbsp;4D; Average method, Supplementary Fig.\u0026nbsp;4C; Complete method, Supplementary Fig.\u0026nbsp;4D; Mcquitty method, Supplementary Fig.\u0026nbsp;4E), and across different time points (first evaluation, Supplementary Fig.\u0026nbsp;5; last evaluation, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a control we calculated UHCA and PCA of the 11 expressive language abilities along with the \u003cem\u003ehyperactivity\u003c/em\u003e (Supplementary Fig.\u0026nbsp;6), \u003cem\u003ebed-wetting\u003c/em\u003e (Supplementary Fig.\u0026nbsp;7), and \u003cem\u003edemands sameness\u003c/em\u003e (Supplementary Fig.\u0026nbsp;8) items. These items are not related to speech and were therefore expected to form a separate cluster. As anticipated, both UHCA and PCA grouped these items into their own cluster at a significant distance from the three expressive clusters, validating the effectiveness of both clustering techniques.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClustering analysis of language comprehension abilities together with expressive language abilities\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA depicts the UHCA dendrogram of the 15 language comprehension items (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) together with the 11 expressive language items (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The UHCA can be interpreted as either three separate clusters or as six distinct clusters. The PCA of the same items (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) clarifies the existence of six clusters of items. These clusters are identical to those detected by separate clustering of receptive and expressive items: three language comprehension clusters (Command, Modifier, and Syntactic) and three expressive language clusters (Single-Word, Single-Sentence, and Multi-Sentence). This six-cluster solution was stable across multiple seeds and across different time points (first evaluation, Supplementary Fig.\u0026nbsp;9; last evaluation, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLanguage comprehension phenotypes in participants\u003c/h3\u003e\n\u003cp\u003eThe principles underlying participant clustering are the same as those used for clustering abilities: individuals with similar patterns of abilities are automatically grouped into hierarchical dendrograms, revealing distinct participant phenotypes. A two-dimensional heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) enables the visualization of the relationship between participant phenotypes and language comprehension abilities. The 62,414 participants are shown as columns; the participants\u0026rsquo; dendrogram is shown at the top. The 15 comprehension abilities are shown as rows; the abilities\u0026rsquo; dendrogram is shown on the left (it is the same dendrogram as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). \u003cem\u003eBlue\u003c/em\u003e indicates the presence of a linguistic ability (parent\u0026rsquo;s response\u0026thinsp;=\u0026thinsp;\u003cem\u003every true\u003c/em\u003e); \u003cem\u003ered\u003c/em\u003e indicates the lack of a linguistic ability (parent\u0026rsquo;s response\u0026thinsp;=\u0026thinsp;\u003cem\u003enot true\u003c/em\u003e); and \u003cem\u003eyellow\u003c/em\u003e indicates an intermittent presence of a linguistic ability (parent\u0026rsquo;s response\u0026thinsp;=\u0026thinsp;\u003cem\u003esomewhat true\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e The three clusters of participants match the three language comprehension mechanisms. The middle cluster of participants (marked Syntactic Phenotype) shows the predominant blue color (representing good skills) across all three language comprehension mechanisms, indicating that these participants have acquired the Command, Modifier, and Syntactic Mechanisms (17.4% of participants, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComprehension Phenotypes. Participant cluster statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyntactic-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModifier-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommand-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e62414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent of Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Mean(SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.7(2.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent Male\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e75.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe leftmost cluster of participants (marked Command Phenotype) shows the predominant blue color only among the Command Mechanism items and red colors across the Syntactic and Modifier Mechanisms items, indicating that these individuals have acquired only the Command Mechanism (35.6%).\u003c/p\u003e \u003cp\u003eThe rightmost cluster of participants (marked Modifier Phenotype) shows the predominant blue color only across the Command and Modifier Mechanisms items and yellow to red colors across the Syntactic Mechanism items, indicating that these individuals have acquired only the Command and Modifier Mechanisms (47%).\u003c/p\u003e \u003cp\u003eThe three language comprehension phenotypes\u0026mdash;Command, Modifier, and Syntactic\u0026mdash;are congruent to those found in previous studies \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows phenotype assignment of participants by their diagnosis. As expected, the most-advanced Syntactic Phenotype contained more participants diagnosed with mild ASD (74.9% mild ASD vs. 5.3% severe ASD). Conversely, the Command Phenotype contained a comparable number of participants diagnosed with mild and severe ASD (33.6% mild ASD vs. 30.4% severe ASD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eASD severity distribution (%) within the three language comprehension phenotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyntactic-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModifier-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommand-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMild ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSevere ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eExpressive language phenotypes in participants\u003c/h3\u003e\n\u003cp\u003e A two-dimensional heatmap relating participants to their expressive language abilities reveals four expressive language phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the dendrogram is displayed horizontally, at the top). The four clusters of participants match the three clusters of linguistic abilities (the abilities\u0026rsquo; dendrogram is shown on the left; it is the same dendrogram as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The leftmost cluster of participants shows the predominant blue color (indicating competent skills) across all three clusters of expressive abilities and is therefore termed the Multi-Sentence Phenotype (13.2% of participants, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The next cluster of participants shows the predominant blue color among Single-Word items and Single-Sentence items and is therefore termed the Single-Sentence Phenotype (24.1%). The third cluster of participants shows the predominant red colors across all expressive abilities and is therefore termed the Nonverbal Phenotype (28.4%). The right-most cluster of participants shows the predominant blue color only across Single-Word items and is therefore termed the Single-Word Phenotype (34.4%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant statistics in the four expressive language clusters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Sentence-Expression Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle-Sentence-Expression Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle-Word-Expression Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNonverbal Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e62414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent of Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Mean(SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.7 (2.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent Male\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e75.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows cluster assignment of participants by their diagnosis. As expected, the most-advanced Muti-Sentence Phenotype contained more participants diagnosed with mild ASD (78.8% mild ASD vs. 5% severe ASD). Conversely, the Nonverbal Phenotype contained more participants diagnosed with severe ASD (29.7% mild ASD vs. 34.4% severe ASD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eASD severity distribution (%) within the four expressive language phenotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Sentence-Expression Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle-Sentence-Expression Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle-Word-Expression Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNonverbal Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMild ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSevere ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCombined receptive-expressive language phenotypes in participants\u003c/h2\u003e \u003cp\u003e A two-dimensional heatmap relating participants to linguistic abilities reveals four distinct receptive-expressive language phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, horizontally, at the top). The leftmost cluster of participants shows the predominant blue color (indicating competency) across all six clusters of linguistic items and is therefore termed the Multi-Sentence-Expression with Syntactic-comprehension Phenotype (marked on Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e as \u0026ldquo;Multi-Sentence\u0026thinsp;+\u0026thinsp;Syntactic;\u0026rdquo; 9.7% of participants, Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant cluster statistics in the four receptive-expressive phenotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Sentence-Expression with Syntactic-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle-Sentence-Expression with Modifier-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle-Word- Expression with Modifier-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNonverbal with Command-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e62414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent of Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.7 (2.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent Male\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e75.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe next cluster of participants shows the predominant blue color across Command- and Modifier-comprehension items as well as among the Single-Word- and Single-Sentence-Expression items. This cluster was therefore termed the Single-Sentence-Expression with Modifier-comprehension Phenotype (Single-Sentence\u0026thinsp;+\u0026thinsp;Modifier; 25.4%).\u003c/p\u003e \u003cp\u003eThe third cluster of participants shows red color as predominant across all the receptive and expressive items, except the Command-comprehension items where this cluster shows the predominant blue color. Consequently, it is termed the Nonverbal with Command-comprehension Phenotype (Nonverbal\u0026thinsp;+\u0026thinsp;Command; 30.5%).\u003c/p\u003e \u003cp\u003eThe rightmost cluster of participants shows the predominant blue color across Command- and Modifier-comprehension items, and Single-Word-Expression items. This cluster was therefore termed the Single-Word-Expression Phenotype with Modifier-comprehension (Single-Word\u0026thinsp;+\u0026thinsp;Modifier; 22.3%).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows cluster assignment of participants by their diagnosis. As expected, the most-advanced Multi-Sentence-Expression with Syntactic-comprehension Phenotype contained more participants diagnosed with mild ASD (80.4% mild ASD vs. 4.0% severe ASD). Conversely, the Nonverbal with Command-comprehension Phenotype contained more participants diagnosed with severe ASD (29.4% mild ASD vs. 34.3% severe ASD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eASD severity distribution (%) within the four receptive-expressive phenotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Sentence-Expression with Syntactic-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle-Sentence-Expression with Modifier-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle-Word- Expression with Modifier-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNonverbal with Command-comprehension Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMild ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSevere ASD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReceptive-expressive phenotypes\u003c/h2\u003e \u003cp\u003eClustering analysis of 62,414 autistic participants based on both receptive and expressive items identified four phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e): (1) \u003cem\u003eMulti-Sentence with Syntactic-\u003c/em\u003ecomprehension; (2) \u003cem\u003eSingle-Sentence with Modifier-comprehension\u003c/em\u003e; (3) \u003cem\u003eSingle-Word with Modifier-comprehension\u003c/em\u003e; and (4) \u003cem\u003eNonverbal with Command-comprehension\u003c/em\u003e. Within the tripartite framework, phenotype (1) corresponds to Verbal Individuals without Structural Language Impairment; phenotype (2) corresponds to Verbal Individuals with Structural Language Impairment, and phenotypes (3) and (4) correspond to Minimally Verbal Individuals.\u003c/p\u003e \u003cp\u003eThese four phenotypes identified in this study closely parallel the four latent classes reported by Munson et al. \u003csup\u003e26\u003c/sup\u003e in their analysis of 456 autistic children using Mullen\u0026rsquo;s Expressive Language, Receptive Language, Visual Reception, and Fine Movement scales. However, comparisons between the two studies must take into account important differences in measurement. Although the Mullen Expressive Language scale is conceptually similar to the expressive language measure used here, the Mullen Receptive Language scale differs substantially from the syntactic language comprehension scale employed in this study (MSEC \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e). The MSEC emphasizes combinatorial language processing\u0026mdash;that is, the ability to derive meaning from word order and syntactic relations (e.g., \u0026ldquo;a cat ate a mouse\u0026rdquo; vs. \u0026ldquo;a mouse ate a cat\u0026rdquo;). In contrast, the Mullen Receptive Language scale primarily assesses functional comprehension of individual words, labels, and categories, with relatively few items probing combinatorial or syntactic processing. Consequently, the construct captured by the MSEC aligns more closely with the Mullen Receptive Language scale only when considered jointly with the Mullen Visual Reception scale, which indexes nonverbal reasoning demands relevant to syntactic interpretation.\u003c/p\u003e \u003cp\u003eUnder this alignment, the phenotypes identified in the present study correspond to those of Munson et al. \u003csup\u003e26\u003c/sup\u003e as follows:\u003c/p\u003e \u003cp\u003e1) \u003cem\u003eMulti-Sentence-Expression with Syntactic-comprehension Phenotype\u003c/em\u003e corresponds to the High Verbal / High Nonverbal group, characterized by mean Mullen Expressive, Receptive, and Visual Reception IQs of approximately 87, 90, and 110, respectively.\u003c/p\u003e \u003cp\u003e2) \u003cem\u003eSingle-Sentence-Expression with Modifier-comprehension\u003c/em\u003e corresponds to the Medium Verbal / Medium Nonverbal group, with mean Expressive, Receptive, and Visual IQs of approximately 62, 64, and 70.\u003c/p\u003e \u003cp\u003e3) \u003cem\u003eSingle-Word-Expression with Modifier-comprehension\u003c/em\u003e corresponds to the Low Verbal / Medium Nonverbal group, with mean Expressive, Receptive, and Visual IQs of approximately 40, 35, and 75.\u003c/p\u003e \u003cp\u003e4) \u003cem\u003eNonverbal with Command-comprehension\u003c/em\u003e corresponds to the Low Verbal / Low Nonverbal group, characterized by mean Mullen Expressive Language, Receptive Language, and Visual IQs of approximately 30, 27, and 50.\u003c/p\u003e \u003cp\u003eThe convergence between the receptive\u0026ndash;expressive phenotypes identified in this study and those reported by Munson et al. \u003csup\u003e26\u003c/sup\u003e suggest that the traditional tripartite framework oversimplifies the classification of autistic language phenotypes by collapsing the \u003cem\u003eSingle-Word with Modifier-comprehension\u003c/em\u003e phenotype together with the \u003cem\u003eNonverbal with Command-comprehension\u003c/em\u003e phenotype (or, in Munson et al.\u0026rsquo;s terminology, the Low Verbal / Medium Nonverbal and Low Verbal / Low Nonverbal group). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, these two profiles are clearly distinct in both expressive and receptive dimensions.\u003c/p\u003e \u003cp\u003eMoreover, even these four broad phenotypes fail to capture less common but theoretically informative profiles, such as the \u003cem\u003eNonverbal\u003c/em\u003e with \u003cem\u003eSyntactic-comprehension Phenotype\u003c/em\u003e, which includes mute individuals who demonstrate intact syntactic comprehension. These observations indicate that one-dimensional categorical frameworks are insufficient to represent the full heterogeneity of language abilities in autism.\u003c/p\u003e \u003cp\u003eA more accurate and conceptually grounded classification system would therefore move beyond broad categorical groupings and instead adopt a two-dimensional framework, in which expressive ability and language comprehension are treated as independent dimensions. Such an approach would naturally accommodate both common and rare profiles, avoid forced category merging, and more accurately reflect the continuous and multidimensional nature of language abilities in autism. Although this two-dimensional framework is widely used in the field of developmental language disorders, it remains relatively uncommon in autism research.\u003c/p\u003e \u003cp\u003eThis study\u0026rsquo;s analysis confirmed three previously identified \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e comprehension phenotypes: Command, Modifier, and Syntactic (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Individuals with the Command Phenotype were limited to the understanding of single words and simple commands. Those with the Modifier Phenotype additionally demonstrated the ability to integrate nouns with modifiers such as color, size, and number. Participants with the Syntactic Phenotype additionally showed comprehension of spatial prepositions, verb tenses, flexible syntax, possessive pronouns, and complex narratives such as stories and fairy tales. For comparison, typically-developing children acquire the Command Phenotype around 2 years of age, the Modifier Phenotype around 3 years, and the Syntactic Phenotype around 4 years \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeparately, clustering analysis of expressive items identified four expressive phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Participants in the first group had not acquired functional expressive language (Nonverbal Phenotype). The second group primarily used isolated words (Single-Word Phenotype). The third group produced single sentences (Single-Sentence Phenotype), whereas the fourth group exhibited multi-sentence expressive language (Multi-Sentence Phenotype). In typically-developing children, the Single-Word Phenotype may emerge as early as 1.5 years, the Single-Sentence Phenotype by approximately 2 years, and the Multi-Sentence Phenotype by around 3 years of age \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eEpidemiological studies of app users provide access to a large number of individuals, but have obvious downsides, such as relying on parent reports. On one hand, parents may yield to wishful thinking and overestimate their children's abilities \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e; on the other, parents possess a deep understanding of their children. This understanding may be especially important for language comprehension assessment, the evaluation of which in a clinical office can be very tricky. Multiple previous studies suggest that parent reports of language skills do not significantly differ from direct clinicians\u0026rsquo; assessments \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Additionally, studies of our database also indicate the prevalence of consistent and truthful parent reports \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Otherwise, if parents were overestimating their children\u0026rsquo;s linguistic abilities, we would see a much larger proportion of children in most-advanced receptive/expressive phenotypes. The fact that most children landed in most-basic phenotypes speaks in favor of realistic estimates of children\u0026rsquo;s abilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eThis study identified three receptive language phenotypes\u0026mdash;Command, Modifier, and Syntactic\u0026mdash;and four expressive phenotypes\u0026mdash;Nonverbal, Single-Word, Single-Sentence, and Multi-Sentence\u0026mdash;in autistic individuals. These findings indicate that the traditional tripartite classification does not fully capture the diversity of language profiles observed in clinical practice. In contrast, a two-dimensional classification system that jointly considers receptive and expressive language abilities provides a more accurate and nuanced characterization of communicative functioning.\u003c/p\u003e \u003cp\u003eAdopting such a framework would allow clinicians to distinguish individuals who share similar expressive levels but differ substantially in comprehension, or vice versa\u0026mdash;differences that are highly relevant for assessment, prognosis, and intervention planning. By explicitly representing domain-specific strengths and weaknesses, a two-dimensional approach could guide the selection of targeted, individualized language interventions and support more precise goal-setting. Ultimately, this refined classification has the potential to facilitate more personalized therapeutic strategies and improve functional communication outcomes for autistic individuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe wish to thank all participants’ caregivers who found time to complete children’s assessments. The language therapy app used to collect the data presented in this manuscript was made possible by the contributions of Rita Dunn, Alexander Faisman, Jonah Elgart, Lisa Lokshina, and Yulia Dumov.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eAV and EK designed the study. KG, RV, EK, and AV analyzed the data. KG and AV wrote the paper.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Psychiatric Association. \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders (DSM-5\u0026reg;)\u003c/em\u003e. (American Psychiatric Pub, 2013).\u003c/li\u003e\n\u003cli\u003eKozlowski, A. M., Matson, J. L., Horovitz, M., Worley, J. A. \u0026amp; Neal, D. Parents\u0026rsquo; first concerns of their child\u0026rsquo;s development in toddlers with autism spectrum disorders. \u003cem\u003eDev. Neurorehabilitation\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 72\u0026ndash;78 (2011).\u003c/li\u003e\n\u003cli\u003eChow, J. C., Broda, M. D., Granger, K. L., Deering, B. T. \u0026amp; Dunn, K. T. Language skills and friendships in kindergarten classrooms: A social network analysis. \u003cem\u003eSch. 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Genet.\u003c/em\u003e \u003cstrong\u003e153B\u003c/strong\u003e, 1119\u0026ndash;1126 (2010).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"language interventions, ASD, language therapy, syntactic language, recursive language","lastPublishedDoi":"10.21203/rs.3.rs-8466583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8466583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnraveling the phenotypic complexity of autism is essential for advancing our understanding of its underlying biology, developmental trajectories, and diverse clinical presentations. Traditional classifications of language profiles in autism distinguish three groups: 1) verbal individuals without structural language impairment, 2) verbal individuals with structural language impairment, and 3) minimally verbal individuals. However, this tripartite framework hides substantial linguistic heterogeneity. Leveraging data-driven clustering on novel comprehensive syntactic language assessment from a large cohort of over 62,414 autistic individuals aged 4\u0026ndash;21 years, we have identified more nuanced, clinically meaningful subtypes. For receptive language, three distinct phenotypes emerged: 1) Command, limited to understanding single words and simple commands; 2) Modifier, that extends to integrating nouns with adjectives but lacks full syntactic processing; and 3) Syntactic, supporting integration of nouns with spatial prepositions and complex syntactic structures. For expressive language, four phenotypes were delineated: 1) Nonverbal, 2) Single-Word, 3) Single-Sentence, and 4) Multi-Sentence. Adopting a two-dimensional framework that separately evaluates receptive and expressive abilities offers greater precision than the conventional tripartite system. This approach better captures individual variability and can guide more targeted language interventions addressing specific strengths and deficits in each domain. Such refinement holds promise for personalized therapies and improved outcomes.\u003c/p\u003e","manuscriptTitle":"Receptive and expressive language phenotyping in over 62,000 autistic Individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 17:30:46","doi":"10.21203/rs.3.rs-8466583/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d231ea9-7263-409a-afe3-6c44ee79f666","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61302448,"name":"Biological sciences/Neuroscience"},{"id":61302449,"name":"Biological sciences/Psychology"},{"id":61302450,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-08T09:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 17:30:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8466583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8466583","identity":"rs-8466583","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}