Investigating Electroencephalographic Predictors of Default Mode Network Anticorrelation in Healthy Adults
Study Details
Study Description
Brief Summary
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
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 Design
Arms and Interventions
| 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.
|
Outcome Measures
Primary Outcome Measures
- Number of participants with EEG measurements that are predictive of the default mode network brain activity measured with fMRI [Two sessions over one month]
Using a machine learning analysis, the investigators will determine the degree to which features within EEG signals can approximate fMRI (default mode network antagonism) while participants perform cognitive tasks and brain activity is recorded with simultaneous EEG-fMRI. Model predictions (EEG prediction of fMRI) within each participant will be 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).
Eligibility Criteria
Criteria
Inclusion Criteria:
- Age between 18-35
Exclusion Criteria:
-
History of psychiatric or neurological disorder
-
contraindication for MRI
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Temple University Brain Research and Imaging Center | Philadelphia | Pennsylvania | United States | 19104 |
Sponsors and Collaborators
- Drexel University
- National Institute of Mental Health (NIMH)
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2208009389
- 1R21MH127384-01A1