Social Responsiveness Scale (SRS) in Relation to Longitudinal Cortical Thickness Changes in Autism Spectrum Disorder

Prigge, Molly B.D.; Bigler, Erin D.; Travers, Brittany G.; Froehlich, Alyson; Abildskov, Tracy J.; Anderson, Jeffrey S.; Alexander, Andrew L.; Lange, Nicholas; Lainhart, Janet E.; Zielinski, Brandon A.

doi:10.1007/s10803-018-3566-1

Cited by 22 publications

(25 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The lack of correlation between symptom severity and the DDI defined by anatomical metrics in our sample should not be taken as a disagreement with previous studies suggesting links between anatomical metrics and behavior [13,[87][88][89]. It is essential to note that we did not test whether symptom severity correlated with the diffusion and anatomical metrics themselves, or whether the brain measures accurately classified diagnosis.…”

Section: Multivariate Brain Maturationcontrasting

confidence: 85%

Deviation from normative brain development is associated with symptom severity in autism spectrum disorder

et al. 2019

View full text Add to dashboard Cite

Background: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods: The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity.Results: Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = − 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations: This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions: Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.

show abstract

Section: Multivariate Brain Maturationcontrasting

confidence: 85%

Deviation from normative brain development is associated with symptom severity in autism spectrum disorder

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Similarly, Wee, Wang, Shi, Yap, and Shen (2014) reported that morphological abnormalities in a set of cortical regions were the most discriminative features for the classification of ASD between 5 to 23 years of age. Using a multi-kernel learning strategy for feature selection, classification based on regional and interregional (Hyde et al, 2010), pars orbitalis (Caeyenberghs et al, 2016), pars triangularis (Jiao et al, 2010), medial orbitofrontal (Hyde et al, 2010;Jiao et al, 2010), middle temporal gyrus (Abell et al, 1999;Yang et al, 2016), inferior temporal gyrus (Abell et al, 1999;Prigge et al, 2018;Yang et al, 2016), fusiform gyrus (Hyde et al, 2010), inferior parietal lobule (Hyde et al, 2010), supramarginal gyrus (Zielinski et al, 2014), lingual gyrus (Hyde et al, 2010;Prigge et al, 2018;Zielinski et al, 2014), cuneus cortex (Zielinski et al, 2014), and pericalcerine cortex (Prigge et al, 2018). Together with previous reports of similar clusters of cortical features (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…features in unseen samples achieved a sensitivity of 95.5% and specificity of 97%, with an accuracy of 96.27% and area under receiver operating characteristic curve (AUC) of 0.995, suggesting acceptable predictive utility. Similar cortical regions underlying individual differences in ASD were identified in the present study in the surface area of the left caudal middle frontal, left supramarginal, right rostral anterior cingulate gyrus, and cortical thickness of the right inferior temporal gyrus, right cuneus, left middle temporal and right fusiform gyrus.Other neuroimaging investigations in ASD have also implicated the identified cortical regions in either or both hemispheres in the anterior cingulate(Haznedar et al, 1997;Jiao et al, 2010;Prigge et al, 2018), posterior cingulate(Hyde, Samson, Evans, & Mottron, 2010;Prigge et al, 2018;Yang, Beam, Pelphrey, Abdullahi, & Jou, 2016), isthmus cingulate(Caeyenberghs et al, 2016;Doyle-Thomas et al, 2013;Yang et al, 2016), insula (Doyle-Thomas et al, 2013), rostral middle frontal gyrus…”

mentioning

confidence: 98%

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

Pua

Ball

Adamson

et al. 2018

Preprint

View full text Add to dashboard Cite

The neurobiology of heterogeneous neurodevelopmental disorders such as autism spectrum disorders (ASD) are still unclear. Despite extensive efforts, most findings are difficult to reproduce due to high levels of individual variance in phenotypic expression.To quantify individual differences in brain morphometry in ASD, we implemented a novel subject-level, distance-based method on subject-specific attributes. In a large multi-cohort sample, each subject with ASD (n=100; n=84 males; mean age: 11.43 years; mean IQ: 110.58) was strictly matched to a control participant (n=100; n=84 males; mean age: 11.43 years; mean IQ: 110.70). Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures were entered into a regularised machine learning pipeline for feature selection, with rigorous out-of-sample validation and bootstrapped permutation testing. Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p=0.01, n=5000 permutations; 10 surface area features, p=0.006, n=5000 permutations). Findings remained robust across subjects and were replicated in validation samples. Identified cortical regions implicate key hubs of the salience and default mode networks as neuroanatomical features of social impairment in ASD.Present results highlight the importance of subject-level markers in ASD, and offer an important step forward in understanding the neurobiology of heterogeneous disorders.

show abstract

“…Here, we used data from the widely used 65-item parent social responsiveness scale (SRS) as a quantitative measure of clinical autistic traits, making it the central variable of interest in our study and in fact the score from which we separate out our autistic traits individuals. The SRS has been proven as a valid measure of autistic traits and thus has been used as a measure of autistic traits (for the purpose of understanding ASD) in several behavioral, genetic, and neuroimaging studies 3,28,32,33,[40][41][42][43][44] . Though not a diagnostic tool, the SRS exhibits high inter-rater and cross-cultural reliability, and correlates greatly with the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) diagnostic assessments for ASD from the DSM-5, making it a robust measure to use in the dimensional study of ASD behaviors 3,33,40,45 .…”

Section: Behavioral Assessmentsmentioning

confidence: 99%

“…This includes early brain overgrowth in frontal and temporal lobes [29][30][31] , causing children to achieve a nearly developed brain volume earlier than controls. Also, longitudinal studies in cortical thickness have shown a general trajectory of accelerated thinning with age in ASD patients versus controls in frontal, temporal, and parietal areas 22,32 . We presume that the general variability and lack of reproducibility is due to the frequently encountered practice of combining heterogeneous ASD patients into one group within casecontrol studies.…”

Section: Introductionmentioning

confidence: 99%

Cortical signatures in behaviorally clustered autistic traits subgroups: a population-based study

et al. 2020

View full text Add to dashboard Cite

Extensive heterogeneity in autism spectrum disorder (ASD) has hindered the characterization of consistent biomarkers, which has led to widespread negative results. Isolating homogenized subtypes could provide insight into underlying biological mechanisms and an overall better understanding of ASD. A total of 1093 participants from the populationbased "Healthy Brain Network" cohort (Child Mind Institute in the New York City area, USA) were selected based on score availability in behaviors relevant to ASD, aged 6-18 and IQ >= 70. All participants underwent an unsupervised clustering analysis on behavioral dimensions to reveal subgroups with ASD traits, identified by the presence of social deficits. Analysis revealed three socially impaired ASD traits subgroups: (1) high in emotionally dysfunctional traits, (2) high in ADHD-like traits, and (3) high in anxiety and depressive symptoms. 527 subjects had good quality structural MRI T1 data. Site effects on cortical features were adjusted using the ComBat method. Neuroimaging analyses compared cortical thickness, gyrification, and surface area, and were controlled for age, gender, and IQ, and corrected for multiple comparisons. Structural neuroimaging analyses contrasting one combined heterogeneous ASD traits group against controls did not yield any significant differences. Unique cortical signatures, however, were observed within each of the three individual ASD traits subgroups versus controls. These observations provide evidence of ASD traits subtypes, and confirm the necessity of applying dimensional approaches to extract meaningful differences, thus reducing heterogeneity and paving the way to better understanding ASD traits.

show abstract

Social Responsiveness Scale (SRS) in Relation to Longitudinal Cortical Thickness Changes in Autism Spectrum Disorder

Cited by 22 publications

References 48 publications

Deviation from normative brain development is associated with symptom severity in autism spectrum disorder

Deviation from normative brain development is associated with symptom severity in autism spectrum disorder

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

Cortical signatures in behaviorally clustered autistic traits subgroups: a population-based study

Contact Info

Product

Resources

About