Thursday, January 1, 2026 1/1/2026

Identifying autism motor deficits in infants using computer vision

Award Number: R21MH136592
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 12/11/2024
PERIOD OF PERFORMANCE END DATE: 11/30/2026

Group Awards By:

View Award Description

Identifying autism motor deficits in infants using computer vision - ABSTRACT Recent work suggests that motor differences in infancy can predict subsequent ASD diagnosis, with the promise of earlier identification of ASD, and faster referral to early intervention than is currently possible. However objectively quantifying these differences has proved challenging. Here we will address this question by leveraging dramatic recent progress in deep learning-driven image processing, combined with emerging techniques in the new field of computational ethology. As a proof of concept, we will apply these techniques to a rich, longitudinal video dataset of semi-structured behavioral assessments on the Autism Observation Scale for Infants (AOSI) from all four sites of the Infant Brain Imaging Study (IBIS). Together this includes videos of more than 400 infants. In Aim 1 we will use OpenPose, a recently developed algorithm for human pose estimation, to automatically extract the location of key infant body joints from each frame of these videos, and then use customized deep-learning approaches to track the movement of the infant from frame to frame. In Aim 2 we will leverage the 24-month diagnostic status and familial liability data to validate the multidimensional computer vision-extracted kinematic data obtained in Aim 1 as ASD motor-function biomarkers. In Aim 3 we will apply unsupervised computational ethology techniques to reveal signatures of ASD in fine motor behavior that will generalize beyond the AOSI paradigm. This will result in a quantitative, precise and naturalistic description of infant motor behavior suitable for predicting ASD diagnosis and severity. By bringing together a computational neuroscientist expert in image processing and behavioral analysis, a clinician-scientist expert in the early development and assessment of autism, and an expert in infant motor development and infant sibling studies for ASD, this work will provide an unbiased and scalable approach to quantifying and categorizing ASD motor function deficits across development, thus critically facilitating high quality and accessible early ASD diagnosis.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $235,342 )
2026	2026	WASHINGTON UNIVERSITY, THE	1 BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Mental Health Research Grants	000	2	12/2/2025	NON-COMPETING CONTINUATION	$235,342
														Subtotal = $235,342

Issue Date FY: 2025 ( Subtotal = $208,888 )
2025	2025	WASHINGTON UNIVERSITY, THE	1 BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Mental Health Research Grants	000	1	12/11/2024	NEW	$208,888
														Subtotal = $208,888

Grand Total All Awards = $444,230

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Identifying autism motor deficits in infants using computer vision

Award Number: R21MH136592

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 12/11/2024

PERIOD OF PERFORMANCE END DATE: 11/30/2026

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer