| The striatal matrix compartment is expanded in autism spectrum disorder. | 10.15154/khn8-jf08 | Background: Autism spectrum disorder (ASD) is the second-most common neurodevelopmental disorder in childhood. This complex developmental disorder that manifests with restricted interests, repetitive behaviors, and difficulties in communication and social awareness. The inherited and acquired causes of ASD impact many and diverse brain regions, challenging efforts to identify a shared neuroanatomical substrate for this range of symptoms. The striatum and its connections are among the most implicated sites of abnormal structure and/or function in ASD. Striatal projection neurons develop in segregated tissue compartments, the matrix and striosome, that are histochemically, pharmacologically, and functionally distinct. Immunohistochemical assessment of ASD and animal models of autism described abnormal matrix:striosome volume ratios, with an possible shift from striosome to matrix volume. Shifting the matrix:striosome ratio could result from expansion in matrix, reduction in striosome, spatial redistribution of the compartments, or a combination of these changes. Each type of ratio-shifting abnormality may predispose to ASD but yield different combinations of ASD features.
Methods: We developed a cohort of 426 children and adults (213 matched ASD-control pairs) and
performed connectivity-based parcellation (diffusion tractography) of the striatum. This identified voxels with matrix-like and striosome-like patterns of structural connectivity.
Results: Matrix-like volume was increased in ASD, with no evident change in the volume or organization of the striosome-like compartment. The inter-compartment volume difference (matrix minus striosome) within each individual was 31% larger in ASD. Matrix-like volume was increased in both caudate and putamen, and in somatotopic zones throughout the rostral-caudal extent of the striatum. Subjects with moderate elevations in ADOS (Autism Diagnostic Observation Schedule) scores had increased matrix-like volume, but those with highly elevated ADOS scores had 3.7-fold larger increases in matrix-like volume.
Conclusions: Matrix and striosome are embedded in distinct structural and functional networks, suggesting that compartment-selective injury or maldevelopment may mediate specific and distinct clinical features. Previously, assessing the striatal compartments in humans required post mortem tissue. Striatal parcellation provides a means to assess neuropsychiatric diseases for compartment-specific abnormalities in vivo. While this ASD cohort had increased matrix-like volume, other mechanisms that shift the matrix:striosome ratio may also increase the chance of developing the diverse social, sensory, and motor phenotypes of ASD.
| 1/2166 | Secondary Analysis | Shared |
| Autistic and Non-autistic Vocabulary Growth Modeling | 10.15154/3cwp-6085 | Background: Autistic children are typically late to develop their expressive vocabulary, but little is known about their early word learning process. This study compared three network growth models on their ability to account for the trajectories of expressive vocabulary acquisition in autistic and non-autistic children.
Methods: We studied expressive vocabularies using item-level data from a child vocabulary checklist (n = 721 records from young autistic children; n = 2,166 records from non-autistic toddlers). We estimated vocabulary growth trajectories for autistic and non-autistic children and assessed the goodness of fit of three models of vocabulary growth, with varying sensitivity to the structure of the environment and the learner’s existing vocabulary knowledge. To do so, we first computed word-level acquisition norms that indicate the vocabulary size at which individual words tend to be learned by each group. Then we evaluated how well network growth models, based on natural language co-occurrence structure and word associations, accounted for variance in the autistic and non-autistic acquisition norms.
Results: Our word-level vocabulary size of acquisition norms closely aligned with age of acquisition data, indicating their utility when age of acquisition norms cannot be derived for neurodivergent populations. Furthermore, we extended key observations and demonstrated that the growth models explained similar amounts of variance in each group. Both groups are biased to learn words that have many connections to words that have been previously learned; however, even after accounting for this learning influence, autistic and non-autistic vocabulary growth trajectories receive an added boost in learning when words are connected to many other words in the learning environment, indicating a similar learning profile.
Conclusions: Both groups preferentially acquire new words by leveraging the semantic structure in the learning environment, indicating an overlap in theoretical accounts of vocabulary growth. | 1/370 | Secondary Analysis | Shared |
| Word Learning and Word Features | 10.15154/1526346 | Vocabulary composition and word-learning biases are closely interrelated in typical development. Learning new words involves attending to certain properties to facilitate word learning. Such word-learning biases are influenced by perceptually and conceptually salient word features, including high imageability, concreteness, and iconicity. This study examined the association of vocabulary knowledge and word features in young children with ASD (n = 280) and typically developing (TD) toddlers (n = 1,054). Secondary analyses were conducted using data from the National Database for Autism Research and the Wordbank database. Expressive vocabulary was measured using the MacArthur-Bates Communicative Development Inventory. Although the trajectories for concreteness, iconicity, and imageability are similar between children with ASD and TD toddlers, divergences were observed. Differences in imageability are seen early but resolve to a common trajectory; differences in iconicity are small but consistent; and differences in concreteness only emerge after both groups reach a simultaneous peak, before converging again. This study reports unique information about the nonlinear growth patterns associated with each word feature for and distinctions in these growth patterns between the groups. | 1/280 | Primary Analysis | Shared |
| Examining the Shape Bias in Young Autistic Children: A Vocabulary Composition Analysis | 10.15154/wjfb-vy71 | Shape is a salient object property and one of the first that children use to categorize objects under one label. Colunga and Sims (2017) suggest that noun vocabulary composition and word learning biases are closely interrelated in typical development. The current study examined the association between noun vocabulary knowledge and perceptual word features, specifically shape and material features. Participants included 249 autistic children and 1,245 non-autistic toddlers who were matched on expressive noun vocabulary size and gender. Nouns were categorized using the Samuelson and Smith (1999) noun feature database. A simple group comparison revealed no group differences in shape bias; both groups evidenced developing noun vocabularies that favored shape+solid and nonsolid+material nouns. However, the trajectory of evidence of shape bias as a function of vocabulary size differed between the groups, with autistic children
demonstrating a reduced shape-bias initially. Future work should examine how children’s learning biases shift over development and whether the shape bias promotes lexical development to the same degree across groups. | 1/249 | Secondary Analysis | Shared |
| Modeling Vocabulary Growth in Autistic and Non-Autistic Children | 10.15154/q64a-9k34 | We assessed the goodness of fit of three models of vocabulary growth, with varying sensitivity to the structure of the environment and the learner’s internal state, to estimated vocabulary growth trajectories in autistic and non-autistic children. We first computed word-level acquisition norms that indicate the vocabulary size at which individual words tend to be learned by each group. We then evaluated how well network growth models based on natural language co-occurrence structure and word associations account for variance in the autistic and non-autistic acquisition norms. In addition to replicating key observations from prior work and observing that the growth models explained similar amounts of variance in each group, we found that autistic vocabulary growth also exhibits growth consistent with “the lure of the associates” model. Thus, both groups leverage semantic structure in the learning environment for vocabulary development, but autistic vocabulary growth is also strongly influenced by existing vocabulary knowledge. | 1/247 | Secondary Analysis | Shared |
| Semantic modeling 2023 | 10.15154/1528994 | Although it is well documented that children with ASD are slower to develop their lexicons, we still have a limited understanding of the structure of early lexical knowledge in children with ASD. The current study uses network analysis and differential item functioning anlaysis to examine the structure of semantic knowledge, which may provide insight into the learning processes that influence early word learning. | 1/208 | Secondary Analysis | Shared |
| Semantic Network Modeling | 10.15154/1522607 | Although it is well documented that children with ASD are slower to develop their lexicons, we still have a limited understanding of the structure of early lexical knowledge in children with ASD. The current study uses network analysis to examine the structure of semantic knowledge, which may provide insight into the learning processes that influence early word learning. | 1/200 | Secondary Analysis | Shared |
| Semantic Network Modeling in Young Autistic Children | 10.15154/z865-cy61 | Background: Most young autistic children have delayed vocabulary growth relative to their non-autistic peers. Additionally, previous studies have revealed that autistic children are less likely to encode associated features of novel objects, suggesting inefficient encoding or different processes for acquiring semantic information about words. Recent network analyses of vocabulary growth revealed important relationships between early vocabulary acquisition and the structure of the sematic environment.
Methods: We studied the expressive vocabularies of 970 non-autistic toddlers (Mage = 20.82 months) and 194 autistic children (Mage = 54.58 months) in two studies. The groups were vocabulary-matched (words produced: MAutistic = 213.60, MNon-autistic = 213.72). In study 1, we estimated their trajectories of semantic development using network analyses. Network structure was based on child-oriented adult-generated word associations. We compared child semantic networks according to indegree, average shortest path length, and clustering coefficient (features that holistically contribute to well-connected network structure). Then, in study 2, we attempted to relate vocabulary-level effects to word-level learning biases.
Results: Study 1 revealed that autistic and non-autistic children are sensitive to the structure of their semantic environment. Both groups demonstrated nonlinear vocabulary trajectories that differed from random acquisition networks. Despite similarities, group differences were observed for each network metric. Differences were most pronounced for clustering coefficient (how closely connected groups of words are), with earlier peaks for autistic children. Study 2 demonstrated that many words differ in their expected vocabulary size of acquisition.
Conclusions: Group differences at the vocabulary- and word-levels indicate that, although autistic children are learning from their semantic environment, they may be processing their semantic environment differently. These deviations indicate that autistic children have distinctive learning biases that may align with core autism features. | 1/194 | Secondary Analysis | Shared |
| Structure of Autistic Children's Semantic Networks | 10.15154/79hg-7d18 | In the current study, we estimated the patterns of semantic development using a network analysis approach. We studied the expressive vocabularies of 815 non-autistic and 163 autistic children who were well-matched on expressive vocabulary. Network structure (i.e., the number of connections between words and patterns of connections) was based on child-oriented word associations. We analyzed networks according to measures that index clusters of word knowledge, connections among words, path lengths between words, and a holistic measure of network structure.
Autistic and non-autistic children are sensitive to the structure of their semantic environment. Both groups demonstrated vocabulary trajectories that differed from random learning networks. However, group differences were observed, with an early peak in the autistic group’s clustering of word knowledge, followed by a sharp decline. Moreover, relative to non-autistic children and across each network measure, we found that autistic children had reduced small-world structure (with reduced clustering of words and longer paths to connect different words). Group differences indicate that, although autistic children are learning from their semantic environment, they may be processing their semantic environment differently. These differences indicate that autistic children have distinctive learning biases.
| 1/163 | Secondary Analysis | Shared |
