# Subnetwork-based Quantitative Imaging Biomarkers for Therapy Assessment in Autism

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $371,982

## Abstract

Project Summary
Autism spectrum disorder (ASD) is a developmental disorder characterized by impairment of social interaction
and communication, as well as repetitive behaviors, with severity ranging from mild to significantly disabling.
The prevalence in the United States is rising (currently about 1 in 68 children) and the associated costs are
great. In our most recent previous efforts on this project, we have initiated development of a graph-based,
Bayesian neuroimage analysis framework and have used it to characterize brain pathology in ASD and identify
abnormal functional subnetworks from groupwise data. Key results demonstrated clear differences in task-
based functional brain networks between ASD and typically developing control (TDC) groups associated with
the perception of biological motion. While our efforts (and those of others) are important for characterizing ASD,
advances in the characterization of response to therapy with imaging are crucial for improved understanding,
and ultimately personalization, of these therapies. Thus, we propose to put forth a bold new direction: to further
develop our analysis methodology and study these task-based subnetworks, now with the goal of characterizing
individuals in terms of their predicted response to treatment. We will focus on Pivotal Response Treatment
(PRT), an intensive behavioral therapy for children with ASD that improves social communication skills. We first
propose to fully develop our unified Bayesian framework to detect both hyper- and hypo-synchronous functional
subnetworks within whole-brain, groupwise, task-based fMRI data on a large training dataset of ASD and TDC
subjects. We will identify dense subgraphs (communities) that exhibit group differences in functional synchrony
between ASD and TDC groups. The groupwise subnetworks will then be mapped to single subject, task-based
fMRI data acquired from a cohort of ASD subjects treated with PRT. For each subject, imaging biomarkers
based on activation signal strength and functional connectivity will be derived for regions within each hyper-
and hypo- synchronous subnetwork at both baseline and after 16 weeks of therapy. Using a random forest
regression strategy, we will use a combination of biomarkers from the baseline data to predict response to
PRT (using change in Social Responsiveness Scale, 2nd Edition as the primary clinical outcome measure). In
addition, we will use a combination of biomarkers from baseline and 16 weeks to predict treatment persistence
at 32 weeks. We will compare the prediction capability of our new approach using task-based fMRI to a set
of biomarkers with regions identified from groupwise analysis of resting state fMRI (rsfMRI) networks found
from the same training subjects noted above using an alternative state-of-the-art method. We will also develop
methods to examine potential metabolic alterations in networks using magnetic resonance spectroscopy (MRS)
of GABA and glutamate (the major excitatory and inhibit...

## Key facts

- **NIH application ID:** 9912861
- **Project number:** 5R01NS035193-22
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** JAMES S DUNCAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $371,982
- **Award type:** 5
- **Project period:** 1996-06-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9912861

## Citation

> US National Institutes of Health, RePORTER application 9912861, Subnetwork-based Quantitative Imaging Biomarkers for Therapy Assessment in Autism (5R01NS035193-22). Retrieved via AI Analytics 2026-06-04 from https://api.ai-analytics.org/grant/nih/9912861. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*