Autism interest intensity in early childhood associates with executive functioning but not reward sensitivity or anxiety symptoms

Godfrey, Kate; Espenhahn, Svenja; Stokoe, Mehak; McMorris, Carly A.; Murias, Kara; McCrimmon, Adam W.; Harris, Ashley D.; Bray, Signe

doi:10.1177/13623613211064372

Cited by 5 publications

(2 citation statements)

References 63 publications

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“…Previous literature suggests that preferred interests can serve as a source of pleasure and learning; both autistic and no diagnosis (NDx) children demonstrate affective responses to preferred interests that can be used promote learning (Cascio et al, 2014; Dunst et al, 2012; Harrop et al, 2019; Hidi et al, 2004; Wood, 2021). Autistic children may be differentiated from NDx peers by challenges in disengaging from preferred interests, demonstrating greater difficulty in cognitive control and attention shifting (Bos et al, 2019; Godfrey et al, 2022; Zhou et al, 2022). Despite being characteristic of autism, preferred interests are observed in typical development (DeLoache et al, 2007).…”

Section: Categorizing and Identifying Preferred Interests In Autistic...mentioning

confidence: 99%

Categorizing and identifying preferred interests in autistic toddlers

Frisch,

Coulter,

Thomas

et al. 2024

Autism Research

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Preferred interests are characteristic of autism spectrum disorder and are reported by parents starting at an early age. However, limited research has explored the presentation of preferred interests in toddlerhood. Previous literature suggests that both the intensity and type of preferred interests held by autistic individuals differ from those held by peers with developmental delay and no diagnosis and that autistic interests are more unusual in nature. While preferred interests are seen in typical child development, previous research suggests that the presence of preferred interests in children with no diagnosis declines with age. Literature also indicates that the sex and cognitive ability of autistic children influences preferred interests. Identification of early preferred interests commonly held by autistic toddlers could serve as a useful clinical indicator of future diagnosis. This article explored whether diagnostic group, age, sex, and cognitive ability predict the likelihood that parents reported preferred interests in children aged 12–36 months with diagnoses of autism, developmental delay, and those with no diagnosis. Additionally, we explored potential diagnostic group differences in interest type. Results suggest that diagnostic group, but not age, sex, or cognitive ability, predicts the likelihood that parents report preferred interests. No differences in the type of interests among diagnostic groups were identified. These results support the use of preferred interests as an early sign of autism but suggest that interest type may not be a helpful clinical indicator of autism in toddlerhood.

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Section: Categorizing and Identifying Preferred Interests In Autistic...mentioning

confidence: 99%

Categorizing and identifying preferred interests in autistic toddlers

Frisch,

Coulter,

Thomas

et al. 2024

Autism Research

View full text Add to dashboard Cite

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“…These datasets have high inter-dataset heterogeneity, encompassing substantial variations in participant characteristics (age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens), imaging parameters (fMRI tasks and sequences), and behavioral measures. We used language abilities and function (EF) as two developmentally and clinically relevant phenotypes for prediction (Adise et al, 2023; Casey, 2023; Godfrey et al, 2022; Qi et al, 2021). We demonstrate that reproducible and generalizable brain-behavior associations using functional connectivity and connectome-based predictive modeling can be realized across diverse dataset features with sample sizes smaller than consortium-levels.…”

Section: Introductionmentioning

confidence: 99%

Brain-phenotype predictions can survive across diverse real-world data

Adkinson,

Rosenblatt,

Dadashkarimi

et al. 2024

Preprint

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Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

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Brief Report: The Impact of Social and News Media Coverage on the Dissemination of Autism Research

Longo

Hand

2022

J Autism Dev Disord

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We examined how sharing autism research articles via social and news media was associated with citations and downloads. We included articles published in 2019 from three autism-focused journals. Every 10 Twitter shares yielded a 4.4% increase in article downloads and 5.2% increase in citations. Articles with at least one Facebook post had 23.3% more downloads than those without. Articles with at least one news story had 56.9% more downloads and 39.3% more citations than those without. Descriptive analysis indicated the most shared, downloaded, and cited articles focused largely on treatments or interventions. Autism researchers should continue sharing articles via Twitter and news media because it increases the reach of their work and may better engage research and autism community members. Supplementary Information The online version contains supplementary material available at 10.1007/s10803-022-05464-8.

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Autism interest intensity in early childhood associates with executive functioning but not reward sensitivity or anxiety symptoms

Cited by 5 publications

References 63 publications

Categorizing and identifying preferred interests in autistic toddlers

Categorizing and identifying preferred interests in autistic toddlers

Brain-phenotype predictions can survive across diverse real-world data

Brief Report: The Impact of Social and News Media Coverage on the Dissemination of Autism Research

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