# Investigating quantitative signatures of autism in toddlers

> **NIH NIH R01** · COLUMBIA UNIVERSITY TEACHERS COLLEGE · 2020 · $366,973

## Abstract

PROJECT SUMMARY/ABSTRACT
Autism Spectrum Disorders (ASD) are complex disorders manifested by qualitatively atypical social
communication skills and an aberrant behavioral repertoire that vary in severity across individuals. We lack
neurobiologically-grounded predictors of autism in the general population. Our studies seek to fill this critical
gap in our knowledge about neurobiologically-grounded quantitative signatures that precede manifestations of
ASD in toddlers recruited from the general population. We aim to (i) apply advanced computational analytic
techniques to formally chart the emergence of atypical developmental trajectories, and (ii) uncover and validate
neurobiologically-grounded, clinically meaningful subtypes predictive of future risk for atypical development,
revolutionizing brain imaging in young children. In our previous work we have discovered that head
movements during functional MRI provide an abundant source of useful movement data whose statistical
features are linked to clinical and cognitive outcomes in children and adults diagnosed with ASD. Our recent
studies have revealed that quantitative signatures of atypical learning trajectories can be detected as early as
1-2 months in infants at high familial risk for developing ASD. Atypical functioning of the sensorimotor system
has deleterious functional consequences across diverse domains of learning and development and may
contribute to ASD manifestations, in toddlers screened prospectively in the general population. Using data from
the NIH-funded National Database for Autism Research (NDAR) we will test whether atypical movement
variability during MRI scans during the 2nd year of life in N=212 toddlers from the general population is
predictive of ASD or non-ASD outcomes (vs. typical development, TD) ascertained during the 3rd year. We will
rigorously quantify key kinematic parameters during MRI scans acquired in toddlers ages 12-24 months
according to different conditions, including sleeping or resting, while language is presented to sleeping
toddlers, and also during a socially-orienting scan. We hypothesize that deleterious, context-incongruent
signatures during the 2nd year of life in toddlers will be related subsequently to greater ASD manifestations at
36-48 months. Machine learning algorithms will be used to classify ASD, non-ASD, and TD toddlers. The
overall goal of these studies is to illuminate the neurobiological basis of sensorimotor variability in toddlers from
the general population and to establish that sensorimotor signatures are part and parcel of the child’s future
ASD diagnosis, a finding which will have profound, transformative implications for neuroimaging methods in
young children. This knowledge will provide new, early mechanistic insights into the basis of such associations
recently established in children, adolescents, and adults with and without ASD, as well as in human infants,
and advance Research Priorities of the NIMH.

## Key facts

- **NIH application ID:** 9866342
- **Project number:** 1R01MH121605-01
- **Recipient organization:** COLUMBIA UNIVERSITY TEACHERS COLLEGE
- **Principal Investigator:** Kristina Denisova
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $366,973
- **Award type:** 1
- **Project period:** 2020-04-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9866342

## Citation

> US National Institutes of Health, RePORTER application 9866342, Investigating quantitative signatures of autism in toddlers (1R01MH121605-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9866342. Licensed CC0.

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