# Objective, MRI biomarkers for pre-symptomatic detection of autism spectrum disorder at 6 months old: commercial software development and optimization

> **NIH NIH R44** · PRIMENEURO, INC. · 2021 · $1,549,232

## Abstract

Project Summary
Autism Spectrum Disorder (ASD) is a broad diagnosis for a disorder characterized by symptoms
affecting repetitive behavior, social communication, and cognitive ability. 1 in 68 children in the
US is affected with ASD (Centers for Disease Control and Prevention, 2017) and the likelihood
that a child will be affected with ASD is 10 times higher if they have a sibling with ASD.
Traditionally, diagnosis occurs most frequently between age 3 to 6 and severe ASD can be
diagnosed as early as 18 months. The infant brain is most plastic and susceptible to intervention
in the first year of life. As such, there exists an infant population that is either diagnosed too late
to receive a more-effective intervention or with a false negative diagnosis, or both. Thus, earlier
prediction of the development of ASD can deliver improved likelihood of favorable outcomes. This
Fast-Track proposal seeks to develop, standardize, and commercialize software methods and
algorithms for neurobehavioral disorder prediction, creating objective biomarkers in a space
dominated by subjective neurobehavioral testing and providing a software as a medical device
that is a single, integrated, easy-to-use solution for MRI processing, analysis, and syndrome
prediction. To create a Minimum Viable Product (MVP), we will: pull existing, academically vetted
methods and software into a commercial design control process (Phase I, SA1); verify the
integrated, full-stack solution on a set of IBIS Network data sets stored in the National Database
for Autism Research (NDAR) and evaluate potential sources of classification error (Phase I, SA2);
and conduct an Alpha release to IBIS Network sites for user interface and human factors feedback
(Phase I, SA3).We will build the MVP into a commercially viable product as we: expand the utility
of the pipeline to accommodate data sets not acquired at IBIS Network sites, optimize manual
Quality Control (QC) workflows through semi-automated user interface design, integrate cortical
surface area, cortical volume, functional connectivity, and other measurements into a common
user interface and workflow, migrate image processing, machine learning, and database
infrastructure to cloud-based tools that can scale on-demand, and iterate on the user experience
given Alpha feedback (Phase II, SA1); evaluate synthetic data designed to approximate results
from multiple MRI scanner types and signal to noise ratio conditions to ensure broad applicability
of the software in clinical settings; expand the utility of the machine learning feature analysis and
classification to include additional features and evaluate non-binary feature spaces, conduct latent
variable analysis to identify one or more scale metrics for ASD prediction; and vet the new metrics
through a thorough review of IBIS Network data and comparison with Alpha release results
(Phase III, SA2); and develop a data report for clinical use and patient education, conduct a Beta
release test across ...

## Key facts

- **NIH application ID:** 10125199
- **Project number:** 5R44MH118763-03
- **Recipient organization:** PRIMENEURO, INC.
- **Principal Investigator:** Maria Bagonis
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,549,232
- **Award type:** 5
- **Project period:** 2018-09-18 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10125199

## Citation

> US National Institutes of Health, RePORTER application 10125199, Objective, MRI biomarkers for pre-symptomatic detection of autism spectrum disorder at 6 months old: commercial software development and optimization (5R44MH118763-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10125199. Licensed CC0.

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