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 that could critically advance personalized MDD treatments.

Public Health Relevance

The characterization of circuit dynamics underlying mood states in major depression and the acute modifiability of circuit features is an innovative and poorly understood area of research. Using an unparalleled dataset of invasive multi-day neural recordings from 160 contacts in patients with major depression who are taking part in a clinical trial of closed loop deep brain stimulation (PRESDIO trial), we seek to identify a set of neural connectivity and activity features that characterize symptom severity states, and to determine whether circuit features can be acutely modified by brain stimulation. This work has high translational potential for development of novel circuit-based neuromodulation therapies for major depression.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Special Emphasis Panel (ZMH1)
Program Officer
Mcmullen, David
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Francisco
Schools of Medicine
San Francisco
United States
Zip Code