# Neuro-computational Approach to Determine a Neurochemical Basis of Mood and Depression

> **NIH NIH R01** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2021 · $383,786

## Abstract

SUMMARY: Depression is the leading cause of disability worldwide, affecting more than 300 million people,
and approximately 20% of the American population. The rate of this brain disorder nearly doubles in patients
with Parkinson’s disease (PD). Patients with depression are characterized by a debilitating negative affective
state and an inability to seek out positive experiences. Unfortunately, the underlying mechanisms are
unknown, but extant treatments suggest a critical role for the dopamine (DA) and serotonin (SE) systems.
The DA and SE systems are known to be a critical for normal learning, reward processing, and choice
behavior. More specifically, circumstantial and mixed evidence supports the hypotheses that DA and SE act as
opponent processes in the human brain, with DA signaling reward prediction errors and SE acting as an
opponent signal. The relationship of these basic ideas to the complex etiology of depression remains unclear.
However, the NIMH’s Research Domain Criteria (RDoC) framework in combination with computational
reinforcement learning theory provides a potential solution to theoretical barriers hindering further investigation.
In this proposal, we will use choice behavior paired with a novel neurochemical sensor to validate two key
domains in the RDoC Matrix: (1) Negative Valence Systems and (2) Positive Valence Systems. The goal will
be to better understand how computations supporting adaptive choice behavior are executed by sub-second
fluctuations in DA and SE in humans and how these signals are altered in patients with depression.
Little is known about rapid microfluctuations in DA and SE in humans or how these signals are altered in the
context of brain disorders like depression and PD. Progress has been hindered by the lack of technology that
permits direct real-time measurements of DA and SE release in humans. To bridge this gap, this proposal will
capitalize on our group’s recent technological innovation, which resulted in the world’s first simultaneous and
co-localized measurements of DA and SE release with sub-second temporal resolution in the human brain.
Herein, we pursue two specific aims, which combine our technological advance with computational
approaches, to validate RDoC subconstructs as they may or may not relate to changes in sub-second DA and
SE signaling in PD patients with versus without depression. In Aim 1, we will examine choice behavior (on
three tasks that incorporate subjective self-reports about subjective mood) and associated DA and SE
signaling in the striatum in PD patients without depression. In Aim 2, we will repeat the same measures, but in
patients with co-morbid symptoms of depression and compare results across the two cohorts. The experiments
proposed may yield unprecedented insight into the function of the DA and SE systems in humans; but, also,
directly assess how these signals may be altered in humans afflicted with depression.

## Key facts

- **NIH application ID:** 10207402
- **Project number:** 5R01MH121099-03
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Kenneth Tucker Kishida
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $383,786
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10207402

## Citation

> US National Institutes of Health, RePORTER application 10207402, Neuro-computational Approach to Determine a Neurochemical Basis of Mood and Depression (5R01MH121099-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10207402. Licensed CC0.

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