NCT06953362

Deciphering Principles of Network Dynamics Underlying Depression Symptom Severity From Multi-day Intracranial Recordings in Patients With Major Depression

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 6 (actual)
• Sponsor: University of California, San Francisco · Academic / Other
• Sex: All
• Age: 22 Months – 70 Years
• Healthy volunteers: Not accepted

Summary

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 the current project, we will leverage the opportunity to study MDD circuit principles from cortical and deep brain structures over a multi-day time period. In this ancillary study to the parent clinical trial, we carry out a set of experiments that establish basic principles of network dynamics underlying MDD from direct neural recordings. This study 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. This project builds on our preliminary findings in two aims. In Aim 1, we 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.

Detailed description

Background: Major depression (MDD) is a leading cause of disability worldwide, with high rates of treatment resistance. This speaks strongly for the need to improve our understanding of the causes and underlying neurobiology of this condition so that we can developed improved treatments. Our current understanding of MDD is quite limited. However, available evidence points to 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. Whether there are neural signatures of circuits that define MDD symptom severity states and the extent to which these circuits are modifiable using direct electrical stimulation are unanswered questions critical for therapeutic advancement. Intracranial EEG (iEEG) offers a promising high-resolution method to study network fundamentals and help elucidate the neural dysfunction underlying MDD. For the first time, we have the opportunity to use this technique to study circuits in MDD in patients participating in a clinical trial of personalized responsive neurostimulation for treatment resistant depression (PRESIDIO). In stage 1, participants are implanted with 160 electrodes across 10 brain sites for 10 days to target brain site placement of chronic deep brain stimulation leads in stage 2. This stage offers the unique opportunity to study principles of MDD circuits from cortical and deep brain structures over a multi-day time period. The current study capitalizes on that opportunity. It is an ancillary study to the PRESIDIO trial where we will have the opportunity to perform a set of experiments that establish basic principles of network dynamics underlying MDD from direct neural recordings. This project is organized around the principal concept that brain circuit dysfunction is reflected in abnormal signatures of functional connectivity and rhythmic local-field activity. The overarching objective is to establish proof-of concept for two fundamental principles of circuit function in depression: i) that circuit features are related to MDD symptom severity, and ii) that perturbation with focal electrical stimulation can acutely modify these circuits. Our rationale for this study is based on: 1) preliminary work, in which we identified a set of connectivity and activity features that were predictive of a diagnosis of co-morbid depression in patients with epilepsy; 2) published work by our group that found targeted stimulation acutely changes mood and spectral power across a broad network in patients with epilepsy; and 3) pilot data for this proposal in which we establish proof-of-concept that MDD symptom severity states can be defined using a 6-question version of the Hamilton Depression Rating Scale (HAMD6). It remains unknown whether circuit features identified in an epilepsy population are generalizable to patients with MDD in the absence of epilepsy, and the manner in which these neural features vary with symptom states - questions we can address in this proposal. We hypothesize that a set of neural connectivity and activity features will characterize symptom severity states in MDD implicating their potential to serve as a state vs. trait MDD biomarker, and that circuit features can be acutely modified by brain stimulation indicating their potential to serve as a therapy target. Goals: 1. Aim 1. Biomarker Development: Characterize the pattern of network connectivity and activity that is associated with symptom severity. The objective of this goal is to establish proof-of concept that circuit features are related to major depressive disorder (MDD) symptom severity. Our working hypothesis is that spectral activity and connectivity within and across the amygdala, hippocampus, SGC, OFC, and VC will capture the majority of variance in MDD symptom severity. The rationale behind this goal is preliminary work that implicates corticolimbic circuit features in diagnostic biomarkers in MDD, and our pilot data showing features to be predictive of symptom state. 2. Aim 2. Acute Change with Circuit Perturbation: Characterize the capacity for brief (2-10 min) periods of targeted electrical stimulation to acutely modify connectivity and activity. The objective of this goal is to establish proof-of-concept that targeted electrical stimulation can acutely modify circuit activity and connectivity features indicating their potential to serve as therapy targets. Our working hypothesis is that graph theoretic properties of MDD network nodes will distinguish their capacity to affect network connectivity and activity following stimulation. The rationale is work by our group that found stimulation to corticolimbic targets acutely changed spectral power across a network, and that nodal properties exert predictive effects of stimulation on networks in epilepsy. A node with high outdegree is a hub of high network influence and could be indicative of a good treatment target. In contrast, a node with high indegree suggests a region that is influenced by stimulation in other regions and may play a role in sensing modifiable neural signatures of MDD.

Conditions

• Treatment Resistant Depression

Interventions

Type	Name	Description
DEVICE	Direct Neural Electrical Stimulation	We are not delivering treatment in this study. Direct neural electrical stimulation is being administered to determine its effect on neural circuit activity and connectivity.

Timeline

• Start date: 2021-01-01
• Primary completion: 2024-12-31
• Completion: 2024-12-31
• First posted: 2025-05-01
• Last updated: 2025-05-01

Locations

1 site across 1 country: United States