# Dynamic Functional Image-based Deep Learning for Therapy Assessment in Autism

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $637,183

## 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 54 children) and the associated costs are
enormous. In our most recent previous efforts on this project, we have advanced methods for the analysis of
functional magnetic resonance imaging (fMRI), using both task-based and resting-state data, for classification,
facilitating identification of ASD biomarkers, and using these biomarkers for personalized outcome prediction
for Pivotal Response Treatment (PRT), an evidence-based form of behavioral therapy for ASD. We have made
great strides with our most recent work focusing on deep learning techniques for extracting biomarkers and
predicting outcome using novel strategies focusing on temporal characteristics with Long Short Term Memory
(LSTM) networks as well as spatial characteristics using Graph Neural Networks (GNNs). Improved use and
characterization of the dynamic changes in connectivity appear crucial for advancing performance based both on
our work and the literature. Thus, in our proposed work, we intend to develop a richer, integrated model that can
more fully exploit the complete spatiotemporal characteristics of the data and its inherent dynamics. In addition, a
key issue in deep learning strategies is access to large datasets which remains a challenge, especially given that
ASD is a spectrum with a range of characteristics, severity and comorbidities and that fMRI is a powerful tool that
encompasses many task-based and resting state acquisition paradigms. Comorbidities are a particular challenge
and opportunity in that they have the potential to increase our understanding of the heterogeneous manifestations
of ASD. We propose to further develop and expand the power and impact of our methodology by broadening our
subject base to include ASD comorbidities (e.g. anxiety, ADHD, depression) and multiple treatment strategies. We
will advance our technology by creating a more integrated spatiotemporal analysis combining our LSTM and GNN
approaches. We will use domain adaptation methods to properly exploit multiple fMRI paradigms in conjunction
with secure federated learning strategies to facilitate multi-institutional data usage with privacy. These innovative
approaches will allow us to improve the practicability of predicting quantitative treatment outcomes in ASD and
measuring associated neuroimaging biomarkers.

## Key facts

- **NIH application ID:** 10439304
- **Project number:** 2R01NS035193-23A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** JAMES S DUNCAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $637,183
- **Award type:** 2
- **Project period:** 1996-06-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10439304

## Citation

> US National Institutes of Health, RePORTER application 10439304, Dynamic Functional Image-based Deep Learning for Therapy Assessment in Autism (2R01NS035193-23A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10439304. Licensed CC0.

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