# Bayesian neurobehavioral phenotyping: from mechanism identification to personalized neuromodulation treatments

> **NIH NIH R61** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2024 · $408,302

## Abstract

PROJECT SUMMARY
Dysregulated activity within the neural networks that underlie reward and executive functions can increase
vulnerability to compulsive drug use. Repetitive transcranial magnetic stimulation (rTMS) can selectively
modulate neural activity within both the reward and the executive control networks. Empirical evidence indicates
that both inhibitory rTMS to the ventromedial prefrontal cortex (VMPFC, a key node in the reward circuits) and
excitatory rTMS to the dorsolateral prefrontal cortex (DLPFC, a key node of the executive control network) can
be therapeutic in individuals with SUDs. However, responses to both treatments are highly variable and rTMS’
therapeutic utility for SUDs remains limited. Personalizing rTMS interventions targeting neuromarkers of
dysregulated activity within the reward and cognitive control networks is likely to reduce treatment response
variability and improve treatment outcomes. We have recently identified two robust neuromarkers of reward
responses to drug-related cues and cognitive control during drug-related decisions that reliably predict nicotine
self-administration. The goal of this application is to determine the extent to which these neuromarkers moderate
responses to rTMS. Our central hypothesis is that smokers with high reactivity to drug-related cues will be more
likely to reduce nicotine self-administration after inhibitory rTMS to the VMPFC, whereas smokers with low
cognitive control during drug-related decisions will be more likely to reduce nicotine self-administration after
excitatory rTMS to the DLPFC. In line with the scope of PAR-19-282 (Exploratory Clinical Neuroscience
Research on SUDs), we will use a phased research approach to test our hypothesis. In the R61 phase, we will
determine the extent to which the reward reactivity neuromarker (Aim 1) and the cognitive control (Aim 2)
neuromarker moderate rTMS treatment effects. To test our milestones (i.e., observing a medium or higher effect
size in at least one of the two aims), we will use Bayesian statistical methods. Using a Bayesian approach will
allow us to obtain calibrated probabilities of treatment outcomes and make a principled go/no-go decision in
moving forward to the R33 phase. In the R33 phase, we will determine the synergistic effect of the two
neuromarkers in moderating rTMS treatment effects (Aim 3) and we will create a Bayesian classifier to
personalize future rTMS interventions for SUDs (Aim 4). We anticipate that upon its successful conclusion, this
Phase II trial will contribute to illuminate the psychophysiological mechanisms that drive compulsive drug use
and will yield the fundamental knowledge needed to efficiently develop new personalized rTMS interventions for
SUDs and other disorders characterized by poor impulse control.

## Key facts

- **NIH application ID:** 10804786
- **Project number:** 1R61DA058276-01A1
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** GEORGE KYPRIOTAKIS
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $408,302
- **Award type:** 1
- **Project period:** 2024-09-15 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10804786

## Citation

> US National Institutes of Health, RePORTER application 10804786, Bayesian neurobehavioral phenotyping: from mechanism identification to personalized neuromodulation treatments (1R61DA058276-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10804786. Licensed CC0.

---

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