# Invasive decoding and stimulation of altered reward computations in depression

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $784,447

## Abstract

Abstract
Depression is a highly prevalent mental health disorder that affects millions of people in the US and causes significant
impacts on well-being, rates of disability and health care costs. Despite these substantial impacts and costs, the
effectiveness of current therapeutical options for the treatment of depression is limited. In recent years there have been
efforts to develop deep-brain stimulation (DBS) strategies guided by results from non-invasive imaging studies.
Unfortunately, these have failed to show significant efficacy, likely because of the vast heterogeneity in disease
presentation and the lack of sufficient data to understand the neurobiological causes of the disease. Current approaches
use unspecific biological (pharmacology) or anatomical (DBS) targets, leading to partial effectiveness and side effects.
Patient-specific depictions of the basis of depression would allow tailored treatment designs, but data of sufficient quality
is mostly unavailable with standard approaches to the study of neural function. Recent approaches leverage multi-areal
invasive electrophysiological recordings in neurosurgical epilepsy patients, which often suffer from co-morbid depression,
to collect of high-quality (multi-areal, high signal-to-noise, high temporal resolution) neurophysiological data, and have
the potential to allow patient-specific models of disease and targeted neurostimulation. In addition, machine learning
methods allow mapping this high-dimensional neural data onto patient’s emotional states centrally affected in depression,
and highlight the involvement of single-site and cross-areal activity in limbic regions, including the hippocampus, amygdala
and orbitofrontal cortex. However, decoding methods present several challenges of their own. First, they select features
associated with self-reported mood - a complex, abstract concept that lacks an objective, quantitative foundation and
may be reported differently across patients, making generalization challenging. Second, data-driven methods are not
grounded on current models of brain function, and thus lack mechanistic explanations of disease underpinnings. Third,
mood is reported in the absence of overt behavior, making it difficult to frame the deficits in the behavioral functions
subserved by affected brain areas. Finally, current approaches lack a connection between decoded neural features and
stimulation strategies. Here, we propose to address these challenges by combining distributed iEEG recordings,
reinforcement learning models of decision-making and machine-learning approaches to study reward processing in
relevant brain areas from epilepsy patients with and without comorbid depression. We will examine local activations as
well as circuit dynamics (functional connectivity) in orbitofrontal cortex, amygdala and hippocampus during decision-
making behavior. We will seek to ground neurophysiological data in reinforcement learning models of decision-making to
provide quantitat...

## Key facts

- **NIH application ID:** 10889967
- **Project number:** 5R01MH124763-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Ignacio Saez
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $784,447
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10889967

## Citation

> US National Institutes of Health, RePORTER application 10889967, Invasive decoding and stimulation of altered reward computations in depression (5R01MH124763-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10889967. Licensed CC0.

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