Dataset Information

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.

SUBMITTER: Tunc B

PROVIDER: S-EPMC6907209 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

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

Tunç Birkan B Yankowitz Lisa D LD Parker Drew D Alappatt Jacob A JA Pandey Juhi J Schultz Robert T RT Verma Ragini R

Molecular autism 20191211

<h4>Background</h4>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.<h4>Methods</h4>The developmental changes in anatomical (co ...[more]

PMID: 31867092

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Project description:Tuberous sclerosis complex (TSC) is a rare genetic disorder that confers a high risk for autism spectrum disorders (ASD), with behavioral predictors of ASD emerging early in life. Deviations in structural and functional neural connectivity are highly implicated in both TSC and ASD. For the first time, we explore whether electroencephalographic (EEG) measures of neural network function precede or predict the emergence of ASD in TSC. We determine whether altered brain function (a) is present in infancy in TSC, (b) differentiates infants with TSC based on ASD diagnostic status, and (c) is associated with later cognitive function. We studied 35 infants with TSC (N = 35), and a group of typically developing infants (N = 20) at 12 and 24 months of age. Infants with TSC were later subdivided into ASD and non-ASD groups based on clinical evaluation. We measured features of spontaneous alpha oscillations (6-12 Hz) that are closely associated with neural network development: alpha power, alpha phase coherence (APC), and peak alpha frequency (PAF). Infants with TSC demonstrated reduced interhemispheric APC compared to controls at 12 months of age, and these differences were found to be most pronounced at 24 months in the infants who later developed ASD. Across all infants, PAF at 24 months was associated with verbal and nonverbal cognition at 36 months. Associations between early network function and later neurodevelopmental and cognitive outcomes highlight the potential utility of early scalable EEG markers to identify infants with TSC requiring additional targeted intervention initiated very early in life. Autism Res 2019, 12: 1758-1773. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Approximately half of infants with tuberous sclerosis complex (TSC) develop autism. Here, using EEG, we find that there is a reduction in communication between brain regions during infancy in TSC, and that the infants who show the largest reductions are those who later develop autism. Being able to identify infants who show early signs of disrupted brain development may improve the timing of early prediction and interventions in TSC, and also help us to understand how early brain changes lead to autism.

Project description:ImportanceAutism spectrum disorder (ASD) is 2 to 5 times more common in male individuals than in female individuals. While the male preponderant prevalence of ASD might partially be explained by sex differences in clinical symptoms, etiological models suggest that the biological male phenotype carries a higher intrinsic risk for ASD than the female phenotype. To our knowledge, this hypothesis has never been tested directly, and the neurobiological mechanisms that modulate ASD risk in male individuals and female individuals remain elusive.ObjectivesTo examine the probability of ASD as a function of normative sex-related phenotypic diversity in brain structure and to identify the patterns of sex-related neuroanatomical variability associated with low or high probability of ASD.Design, setting, and participantsThis study examined a cross-sectional sample of 98 right-handed, high-functioning adults with ASD and 98 matched neurotypical control individuals aged 18 to 42 years. A multivariate probabilistic classification approach was used to develop a predictive model of biological sex based on cortical thickness measures assessed via magnetic resonance imaging in neurotypical controls. This normative model was subsequently applied to individuals with ASD. The study dates were June 2005 to October 2009, and this analysis was conducted between June 2015 and July 2016.Main outcomes and measuresSample and population ASD probability estimates as a function of normative sex-related diversity in brain structure, as well as neuroanatomical patterns associated with low or high ASD probability in male individuals and female individuals.ResultsAmong the 98 individuals with ASD, 49 were male and 49 female, with a mean (SD) age of 26.88 (7.18) years. Among the 98 controls, 51 were male and 47 female, with a mean (SD) age of 27.39 (6.44) years. The sample probability of ASD increased significantly with predictive probabilities for the male neuroanatomical brain phenotype. For example, biological female individuals with a more male-typic pattern of brain anatomy were significantly (ie, 3 times) more likely to have ASD than biological female individuals with a characteristically female brain phenotype (P = .72 vs .24, respectively; χ21 = 20.26; P < .001; difference in P values, 0.48; 95% CI, 0.29-0.68). This finding translates to an estimated variability in population prevalence from 0.2% to 1.3%, respectively. Moreover, the patterns of neuroanatomical variability carrying low or high ASD probability were sex specific (eg, in inferior temporal regions, where ASD has different neurobiological underpinnings in male individuals and female individuals).Conclusions and relevanceThese findings highlight the need for considering normative sex-related phenotypic diversity when determining an individual's risk for ASD and provide important novel insights into the neurobiological mechanisms mediating sex differences in ASD prevalence.

Dataset Information

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

Background

Methods

Results

Limitations

Conclusions

Publications

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

Similar Datasets

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