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| ID | Type | Description | Link |
|---|---|---|---|
| 1R21MH127384-01A1 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Mental Health (NIMH) | NIH |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Neurofeedback | Experimental | Subjects will undergo one session where they will visualize real-time feedback of signals recorded from their brains. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Neurofeedback | Behavioral | Participants will visualize real-time feedback of signals recorded from their brains as measured with functional MRI. |
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| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Drexel University | Philadelphia | Pennsylvania | 19104 | United States |
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.
Availability: April 2024 until 2030
Publicly available
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Potential participants were excluded from the study if they were unable to provide consent, reported having a current or history of psychiatric/neurological disorders, a chronic medical condition, were pregnant, were prisoners, were unable to understand English, had metal in the body, were contraindicated for MRI, had allergies to saline gel, or had cold, flu or COVID-19 symptoms within the two weeks preceding participation.
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| ID | Title | Description |
|---|---|---|
| FG000 | EEG-fMRI | Subjects underwent two sessions that included tasks performed during simultaneous EEG-fMRI recordings |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | EEG-fMRI | Subjects underwent two sessions that included tasks performed during simultaneous EEG-fMRI recordings |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. | All participants were included in analysis. | Posted | Mean | Standard Deviation | Spearman rank correlation coefficient | Two sessions 3 to 62 days |
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From enrollment until final session, up to 62 days
ClinicalTrials.gov definitions were used.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | EEG-fMRI | Subjects underwent two sessions that included tasks performed during simultaneous EEG-fMRI recordings |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Aaron Kucyi | Drexel University | 2155537124 | aaron.kucyi@drexel.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Nov 12, 2025 | Nov 12, 2025 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Sep 22, 2022 | Oct 29, 2025 | ICF_001.pdf |
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| ID | Term |
|---|---|
| D058765 | Neurofeedback |
| ID | Term |
|---|---|
| D001676 | Biofeedback, Psychology |
| D026441 | Mind-Body Therapies |
| D000529 | Complementary Therapies |
| D013812 | Therapeutics |
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| Years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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Subjects underwent two sessions that included tasks performed during simultaneous EEG-fMRI recordings |
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| 0 |
| 24 |
| 0 |
| 24 |
| 0 |
| 24 |
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| D001521 |
| Behavior Therapy |
| D011613 | Psychotherapy |
| D004191 | Behavioral Disciplines and Activities |
| D030141 | Feedback, Psychological |