# Utilizing changes in human brain connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain stimulation on depression symptoms

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $117,008

## Abstract

PROJECT SUMMARY/ABSTRACT
This is a Diversity Supplement Proposal for Azeezat K. Azeez, Ph.D., entitled “Machine Learning for Predictive
Clinical Outcomes to Neuromodulation Therapy for Treatment-Resistant Depression”. It is a Supplement to the
Parent R01, held by Nolan Williams, MD titled “5R01MH122754-02: Utilizing changes in human brain
connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain
stimulation on depression symptoms”. The goal of the Parent Grant is to (1) test changes in resting-state
functional connectivity (rsFC) using functional magnetic resonance imaging (fMRI) scans daily and (2) examine
how rsFC changes relate to clinical improvement due to a novel and effective neuromodulation intervention,
Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). This will improve our understanding of the
underlying mechanism of Major Depressive Disorder (MDD), particularly Treatment-Resistant Depression.
Notwithstanding the high efficacy of SAINT, relative to existing therapeutics a substantial number of
participants do fail to respond. Failure to respond, particularly in TRD, leads to detrimental health and
economic effects on the participant as well as on the health system. Our lack of ability to predict who will
respond to treatment constitutes a major translational gap in the SAINT technology. Therefore, the goal of the
current diversity supplement is to employ machine learning algorithms on neuroimaging data to predict who is
most likely to respond to treatment. Data science, neuroimaging, and neurostimulation are converging at an
exciting junction, the intersection of these disciplines is where the Diversity Supplement lies. A combination of
Machine Learning classifier models (supervised and unsupervised) and selection of appropriate imaging
features, trained on training data, then tested, and validated will yield a model with high accuracy for predicting
clinical outcomes. A combination of these parts will allow us the highest probability of developing a successful
algorithm that can be packaged into software to accompany neuromodulation intervention. The current
Supplement aims to 1) classify Treatment Response between cohorts; Active, Sham, and Neurotypical Control,
and 2) accurately predict remission and response outcomes in Treatment Severity classes. The Diversity
Supplement would allow Dr.Azeez to gain proficiency in 1) Machine Learning Techniques, 2) Clinical
Assessments, and 3) Professional Development while under the 2-year funding period. Training and research
for the project will be conducted at Stanford University which offers excellent intellectual and physical
resources to complete the proposed work. The research proposed in the Supplement will help to launch Dr.
Azeez’s career in developing Computational Aids for Clinicians in Psychiatric Medicine. This is a major goal of
the supplement application and one that will prepare the candidate, Dr. Azeez, for the ...

## Key facts

- **NIH application ID:** 10542288
- **Project number:** 3R01MH122754-03S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Nolan R. Williams
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $117,008
- **Award type:** 3
- **Project period:** 2020-04-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10542288

## Citation

> US National Institutes of Health, RePORTER application 10542288, Utilizing changes in human brain connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain stimulation on depression symptoms (3R01MH122754-03S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10542288. Licensed CC0.

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