# Model-based neural control of brain stimulation for neuropsychiatric disorders

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $574,272

## Abstract

PROJECT SUMMARY
Neuropsychiatric disorders are a leading cause of disability worldwide, with depressive disorders being the most
disabling among them. Many patients are resistant to all current treatments. Invasive electrical brain stimulation
for treatment-resistant depression showed early promise in open-label studies but has had variable efficacy in
controlled clinical trials. To date, stimulation in neuropsychiatric disorders has been limited to an open-loop
approach that applies a fixed pattern of continuous stimulation regardless of symptom levels. One limitation is
that open-loop stimulation does not track the inter- and intra-subject variabilities in neuropsychiatric symptoms,
which can change rapidly in an individual. Another limitation is the lack of an input-output model that can guide
stimulation by predicting how ongoing stimulation input drives large-scale neural activity and the symptoms it
underlies in an individual. We will address these limitations to enable precise invasive electrical brain stimulation
for neuropsychiatric disorders by developing a novel real-time model-based neural control system. We will
provide proof-of-concept demonstration for acute control of neural biomarkers of mood states related to
depression symptoms in epilepsy patients with implanted intracranial electroencephalography (iEEG) electrodes,
in whom we will obtain repeated mood self-reports and perform stimulation simultaneously with neural recording.
The system will continuously adjust the stimulation parameters, for the first time, based on 2 elements: (i) Novel
personalized input-output model learned on recorded brain network response while delivering a new stochastic
stimulation waveform to excite network activity. (ii) Personalized decoder trained on multi-day continuous iEEG
recordings and simultaneous mood self-reports to estimate mood state variations from neural activity as
feedback. Combining these, we will build a real-time model-based closed-loop system to precisely drive the
neurally-decoded mood state—the neural biomarker of mood—to a target level. Our system generalizes to any
stimulation site. Here, we will demonstrate the system with orbitofrontal stimulation as we have shown it to
acutely improve mood and modulate large-scale mood-relevant brain activity. We will run real-time closed-loop
experiments in each patient. The system will estimate the neural biomarker from iEEG and adjust the stimulation
amplitude and frequency in real-time based on the input-output model to drive the estimated biomarker to a
target level. We will also develop model-free closed-loop on-off stimulation that turns stimulation on-off based
on the neural biomarker. We will compare model-based, on-off and open-loop stimulations. Success of this
program will enable precisely regulating a desired brain state by developing the first model-based closed-
loop invasive brain stimulation system and advancing neuromodulation technology. It will also directly
inform elec...

## Key facts

- **NIH application ID:** 10376864
- **Project number:** 5R01MH123770-02
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Maryam Shanechi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $574,272
- **Award type:** 5
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10376864

## Citation

> US National Institutes of Health, RePORTER application 10376864, Model-based neural control of brain stimulation for neuropsychiatric disorders (5R01MH123770-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10376864. Licensed CC0.

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