Investigating Electroencephalographic Predictors of Default Mode Network Anticorrelation in Healthy Adults

Investigating Electroencephalographic Predictors of Default Mode Network Anticorrelation for Personalized Neurofeedback

Healthy adult subjects will participate in two sessions. The first session will involve measurements of brain activity using simultaneous recordings with electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI). During brain activity measurement, participants will perform cognitive tasks assessing attention. The second will involve fMRI-based neurofeedback during simultaneous EEG-fMRI recording. Participants will receive real-time visual feedback of signals measured from specific parts of their brain and will try to control that activity.

Study Overview

• Status: Completed
• Conditions: Healthy
• Intervention / Treatment: Behavioral: Neurofeedback

Detailed Description

Neuropsychiatric conditions are increasingly being understood as disorders of intrinsic, functional interactions within and between widespread, distributed, brain networks. Given recent advances in functional Magnetic Resonance Imaging (fMRI) data acquisition and computational analysis, it is now possible to reliably map the functional neuroanatomy of brain networks within individuals, offering a potential avenue for identifying personalized neurotherapeutic targets. However, gold standard treatments (e.g. pharmacotherapy) in current psychiatric practice were not originally designed to target specific brain network interactions and lack protocols that leverage such individual-level data. Real-time neurofeedback- whereby patients observe and learn to regulate selected aspects of their own brain activity- is a candidate approach to personally tailor the normalization of unhealthy communication within and between brain networks. However, to target the major brain networks that function abnormally in neuropsychiatric conditions, neurofeedback relies on fMRI, which is an expensive procedure involving a complex setup and patient burden. The goal of this project is to develop an electroencephalography (EEG) "fingerprint" of fMRI network dynamics so that a neurofeedback system based on EEG (electrodes placed on the scalp) alone can be used to precisely target interactions within and between brain networks. Because EEG devices can be portable and offer relatively simple setup in flexible settings, this research could enable a scalable form of network-based neurofeedback training that patients could regularly access. Aim 1 of this research is identify an optimal model of EEG features that are predictive of fMRI-based default mode network (DMN) "antagonism" within individuals. The investigators focus on this DMN antagonism because it is a major feature that is relevant to cognitive dysfunction in psychiatry disease at a transdiagnostic level. The investigators will collect high-quality, simultaneous EEG-fMRI data in 24 healthy adults (>100 mins of sampling per participant), including three conditions: (1) resting state, (2) continuous task performance, and (3) continuous fMRI-based neurofeedback from DMN antagonism states. The investigators will apply machine learning-based methods to identify an optimal mapping between EEG signal components and fMRI-based DMN antagonism. Further, the investigators will determine how much individual-level EEG-fMRI sampling is needed to successfully predict DMN antagonism from EEG. Aim 2 of the research is to test whether EEG markers of DMN antagonism are predictive of cognitive task performance fluctuations within individuals. As such, the findings could offer validation of the behavioral relevance of an EEG neurofeedback system that would target DMN antagonism. If successful, the work can lead to development of an accessible, computational psychiatry tool that can be tested in clinical conditions in which DMN antagonism (and related cognitive function) is affected, including attention-deficit/hyperactivity disorder, depression and schizophrenia.

• Study Type: Interventional
• Enrollment (Actual): 24
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Pennsylvania
    • Philadelphia, Pennsylvania, United States, 19104
      • Drexel University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 35 years (Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• Age between 18-35

Exclusion Criteria:

• History of psychiatric or neurological disorder
• contraindication for MRI

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Basic Science
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Neurofeedback; Subjects will undergo one session where they will visualize real-time feedback of signals recorded from their brains.	Behavioral: Neurofeedback; Participants will visualize real-time feedback of signals recorded from their brains as measured with functional MRI.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Association Between EEG Measurements and Default Mode Network Brain Activity Measured With fMRI	The investigators determined the degree to which features within EEG signals can approximate fMRI (default mode network activation) while participants performed cognitive tasks and brain activity was recorded with simultaneous EEG-fMRI. Model predictions (EEG prediction of fMRI) within each participant were generated from multiple EEG features, including spectral power in different frequency bands (Theta: 4-7 Hz, Alpha: 8-12 Hz, Beta1: 13-22 Hz, Beta2: 23-29 Hz, Gamma: 30-50 Hz). The average temporal correlation across the two sessions was computed between EEG and fMRI. A higher correlation indicated that EEG was more predictive of fMRI, whereas a lower correlation indicated EEG was less predictive of fMRI.	Two sessions 3 to 62 days

Collaborators and Investigators

• Sponsor: Drexel University
• Collaborators: National Institute of Mental Health (NIMH)

Study record dates

Study Major Dates

• Study Start (Actual): October 6, 2023
• Primary Completion (Actual): February 24, 2025
• Study Completion (Actual): February 24, 2025

Study Registration Dates

• First Submitted: October 18, 2022
• First Submitted That Met QC Criteria: October 21, 2022
• First Posted (Actual): October 24, 2022

Study Record Updates

• Last Update Posted (Actual): March 12, 2026
• Last Update Submitted That Met QC Criteria: February 20, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Therapeutics; Mind-Body Therapies; Complementary Therapies; Behavior Therapy; Psychotherapy; Behavioral Disciplines and Activities; Biofeedback, Psychology; Feedback, Psychological; Neurofeedback
• Other Study ID Numbers: 2208009389; 1R21MH127384-01A1 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The data generated from this study will become publicly available. After de-identifying and anonymizing all neuroimaging, electrophysiological and behavioral data, we plan to share data via the National Institute of Mental Health Data Archive.
• IPD Sharing Time Frame: Availability: April 2024 until 2030
• IPD Sharing Access Criteria: Publicly available
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No