# Deciphering principles of network dynamics underlying depression symptom severity from multi-day intracranial recordings in patients with major depression

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $201,875

## Abstract

PROJECT SUMMARY/ABSTRACT
 Major depressive disorder (MDD) is common and causes significant disability world-wide. While typically
responsive to medications and therapy, there remain a subset of patients who are treatment resistant. Novel
approaches are critical to treat these patients. MDD is likely caused by dysfunction in distributed neural networks,
a perspective consistent with the etiological and diagnostic heterogeneity of this disorder. While imaging and
electroencephalography (EEG) have helped identify MDD circuitry, no consensus has been reached on the
identification of diagnostic biomarkers. Furthermore, the dynamics of MDD circuitry in relation to symptom
severity is unknown. Characterization of circuit signatures that define MDD symptom severity states and the
extent to which these circuits are modifiable using electrical stimulation are critical for therapeutic advancement.
 Intracranial EEG (iEEG) offers a high spatial and temporal resolution method to study depression networks.
For the first time, we have an unparalleled opportunity to study such circuits in MDD patients participating in a
clinical trial of personalized responsive neurostimulation for treatment resistant depression (PRESIDIO). In stage
1 of this trial, participants are implanted with 160 electrodes from 10 sub-chronic intracranial leads across 10
brain sites for 10 days. The goal of this parent study stage is to optimize brain-site targeting for deep brain
stimulation. In this proposal, we will leverage the opportunity to study MDD circuit principles from cortical and
deep brain structures over a multi-day time period.
 In an ancillary study to this parent clinical trial, we propose a set of experiments that establish basic principles
of network dynamics underlying MDD from direct neural recordings. This proposal is organized around the
principal concept that brain circuit dysfunction is reflected in abnormal signatures of functional connectivity and
rhythmic local-field activity. This concept is supported by our pilot work where we found evidence of distinct MDD
networks characterized by functional connectivity and spectral activity. Furthermore, in the first parent trial
participant we successfully mapped MDD circuits at the individual level and found that gamma power in the
amygdala could successfully decode mood state (AUC = 86%). This proposal builds on these preliminary findings
in two aims. In Aim 1, we will characterize state-dependent functional connectivity and spectral activity in relation
to symptom severity. In Aim 2, we will examine the manner and time course in which targeted electrical
stimulation acutely modifies circuits. Together, this research will yield the first characterization of connectivity
and activity dynamics in MDD over a multi-day period from direct neural recordings. This rare insight into MDD
circuity provided by this novel dataset establishes proof-of-concept principles for biomarker development and
therapeutic target selection tha...

## Key facts

- **NIH application ID:** 10321656
- **Project number:** 5R21MH124759-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** ANDREW D KRYSTAL
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $201,875
- **Award type:** 5
- **Project period:** 2021-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10321656

## Citation

> US National Institutes of Health, RePORTER application 10321656, Deciphering principles of network dynamics underlying depression symptom severity from multi-day intracranial recordings in patients with major depression (5R21MH124759-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10321656. Licensed CC0.

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