# Dissecting and modifying temporal dynamics underlying major depressive disorder

> **NIH NIH R01** · DUKE UNIVERSITY · 2022 · $684,616

## Abstract

Title: Dissecting and modifying temporal dynamics underlying major depressive disorder
Multiple human imaging studies have described aberrant spatiotemporal dynamics in specific
brain networks across subjects with major depressive disorder. Furthermore, rodent studies
have identified dysfunctional synchrony across cortical limbic circuits in genetic and stress-
induced models of major depressive disorder. Nevertheless, it remains to be clarified whether
these observed changes in neural dynamics play a causal role or simply reflect (i.e., correlate
with) the behavioral-state observed in major depressive disorder. Several major challenges to
addressing this question exist. 1) The brain synchronizes dynamics across multiple timescales.
Rodent studies classically monitor dynamics at the millisecond time scale (reflecting circuits),
and human studies typically monitor brain dynamics at the seconds time scale (reflect circuit
and network level activity). 2) Rodent studies are generally limited in their ability to monitor
large-scale activity from many brain regions concurrently, while human imaging studies observe
activity across the whole brain. 3) To our knowledge, few approaches/models integrate changes
in cell-type specific gene expression implicated in depression to changes in circuit and network-
specific brain dynamics. 4) Techniques which directly manipulate brain dynamics (neural
synchrony and cross-frequency coupling) have yet to be largely implemented throughout the
rodent research community. To address these challenges, we propose to perform multi-circuit in
vivo neural recordings in the two widely used rodent models of depression. We will then utilize
machine learning to determine the spatiotemporal dynamic alterations that are shared between
the two models. Next, we will test whether cellular molecular manipulations implicated in major
depressive disorder are sufficient to induce the same spatiotemporal dynamic alterations.
Finally, we will verify that these spatiotemporal dynamics are causal by directly inducing and
suppressing them and measuring their impact on behavior. This strategy will yield an
unprecedented understanding of how altered dynamics within specific brain circuits contribute to
depression.

## Key facts

- **NIH application ID:** 10441495
- **Project number:** 5R01MH120158-04
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Kafui Dzirasa
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $684,616
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10441495

## Citation

> US National Institutes of Health, RePORTER application 10441495, Dissecting and modifying temporal dynamics underlying major depressive disorder (5R01MH120158-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10441495. Licensed CC0.

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