# Explainable Machine Learning to Guide Prefrontal Brain Stimulation

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $796,927

## Abstract

Project Summary
Brain stimulation has shown great therapeutic promise for a wide range of neurological and psychiatric disorders. In
addition to advanced engineering tools, successful implementation of brain stimulation requires a comprehensive un-
derstanding of how this treatment drives changes in network dynamics and connectivity at a large scale and across
multiple brain areas. It also requires the design of controllers that can relate stimulation effects to behavior and function.
To achieve these goals, we will develop novel explainable machine learning models for psychiatric brain stimulation.
To do so, we put forward three overarching goals. First, we aim to learn biologically plausible and flexible functional
connectivity models from electrocorticography (ECoG) data. Then, we plan to develop a computational model based
on a deep graph convolutional net to learn associations between ECoG data and network-scale connectivity. We will
then design a machine learning based guide for psychiatric brain stimulation. Finally, we will use our tools to under-
stand how the network evolves through time. To achieve these goals, the project brings together an interdisciplinary
team of investigators with unique expertise in artificial intelligence and machine learning, computational and theoretical
neuroscience, network science and biostatistics, bioengineering and brain stimulation experiments, and interventional
psychiatric and neural engineering. The team will lead experimental and computational efforts that will produce ad-
vanced explainable machine learning solutions informed by brain stimulation experiments and utilize these tools to
design more efficient and effective brain stimulation therapies.

## Key facts

- **NIH application ID:** 10874746
- **Project number:** 5R01MH125429-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Zaid Harchaoui
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $796,927
- **Award type:** 5
- **Project period:** 2022-07-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874746

## Citation

> US National Institutes of Health, RePORTER application 10874746, Explainable Machine Learning to Guide Prefrontal Brain Stimulation (5R01MH125429-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10874746. Licensed CC0.

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