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

Abstract

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.

Individual variation underlying brain age estimates in typical development

“…As with previous studies in this cohort, we did not find strong evidence of association between brain age delta and cognitive performance, after accounting for age and sex [Ball et al, 2017;Khundrakpam et al, 2015]. Other studies have reported small to moderate associations between brain age and measures of cognitive performance during typical development [Erus et al, 2015;Lewis et al, 2018], or associations between brain age estimates and symptoms of psychopathology or neurodevelopmental disorders [Cropley et al, 2020;Tunç et al, 2019]. Our findings do not preclude the detection of brain age-behaviour associations in typically-developing samples, others have employed multi-modal approaches to successfully predict brain age from brain structure and function, additionally using measures of tissue microstructure from diffusion MRI and subcortical metrics rather than just cortical thickness and area [Erus et al, 2015;Lewis et al, 2019;Liem et al, 2017].…”

“…Others have reported very small, or nonsignificant, effect sizes for the association between brain age gap and cognitive performance [Ball et al, 2017;Khundrakpam et al, 2015]. In children with autism spectrum disorder, a negative brain age gap (reflecting that the brain was predicted to be younger than expected for chronological age) was related to greater disorder symptom severity [Tunç et al, 2019]. In a non-typically developing sample of children and adolescents, a positive brain age gap was associated with greater psychopathology symptom severity [Cropley et al, 2020].…”

Evidence against the “normalization” prediction of the early brain overgrowth hypothesis of autism

“…but not all [56]. Using a subset of the CHOP sample reported in the current study, we recently reported that regional deviations from a normative model of brain development in diffusion metrics, volume, thickness, and surface area can accurately classify diagnostic status, although diffusion metrics out-performed anatomical measures in this age-based approach [82]. We plan to further investigate regional differences in cortical surface area and thickness in future work.…”

Controlling litter effects to enhance rigor and reproducibility with rodent models of neurodevelopmental disorders

“…Brain development requires tight coordination of cell proliferation, differentiation, migration, and synapse formation. Any disruption to this complex chain of events has the potential to perturb brain development and increase risk for a neurodevelopmental disorder (NDD), such as autism spectrum disorder, schizophrenia, intellectual disability, attention deficit hyperactivity disorder, or bipolar disorder [ 1 – 3 ]. Genetic and environmental factors influence risk for NDDs and do not operate alone, but rather interact to increase disease risk [ 4 ].…”