Project Details
Description
A major impediment to early identification and intervention for autism spectrum disorder (ASD) is our limited
understanding of how different children present signs as toddlers, including what risk symptoms coincide
across multiple dimensions to predict outcome. Our objectives are to quantify behavioral and brain connectivity
based subtypes of risk that model the variability of ASD symptom expression in a community sample of
toddlers. We will then test the predictive validity of this approach in the same cohort of children at three years
of age in order to identify risk profiles that differentially predict later cognitive, behavioral, and clinical features.
First, we will implement two unsupervised data-driven computational approaches in a community sample of
3000 children between 18-24 months old in order to characterize clusters of risk profiles. We hypothesize that
each approach will identify a proportion of high-risk individuals consistent with epidemiological estimates of
ASD and associated developmental disabilities (e.g., language or global DD). Based on our preliminary data,
we anticipate that ~300 children will be identified by these data-driven risk-profiling methods. We also
hypothesize that distinct patterns of structural and functional connectivity will distinguish groups of at-risk
children and that these groups will differ from low-risk children. All children will be scanned with the same brain
imaging sequences and procedures implemented in the Baby Connectome Project and will be compared to
data from 100 low-risk children from that project. Our neuroimaging sample of 300 children will be reassessed
at age three with direct clinical assessment using gold-standard diagnostic instruments as well as parent
report. This will allow us to validate the risk profiling approach implemented at 18-24 months, to compare with
a current screening approach, and to refine the risk profiling approach with supervised training of prediction
algorithms that incorporates behavioral/clinical outcome data. We expect this method for risk
stratification/subtyping to better model the heterogeneity inherent to the early at-risk and resilient phenotypes,
which will subsequently improve early identification/diagnosis efforts. These outcomes will have translational
impact because improved methods for early identification in ASD are necessary for the successful
development of efficacious, personalized early interventions.
Status | Finished |
---|---|
Effective start/end date | 3/1/19 → 12/31/23 |
Funding
- National Institute of Mental Health: $802,904.00
- National Institute of Mental Health: $740,972.00
- National Institute of Mental Health: $782,692.00
- National Institute of Mental Health: $668,914.00
- National Institute of Mental Health: $668,677.00
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