This project will examine whether individuals can be trained, using real-time feedback about brain function during neuroimaging, to effectively remove thoughts from mind by providing them with a sense of what it feels like to successfully remove a thought.
Right-handed
Speak English fluently
Minimum 8th grade education
Good hearing
History of significant illnesses (including cardiovascular disease, cancer, immunodeficiency disorders (including HIV infection)
Diabetes
Unstable endocrine disorders
Neurological disorders
Parallel
fMRI neurofeedback
Other interventionfMRI neurofeedback is a technique that allows individuals to observe real-time feedback of their brain activity as measured by fMRI scans. During a session, the participant lies inside an MRI scanner while their whole brain is scanned. This data is processed in real-time by a computer and then presented to the participant in the form of visual feedback. This feedback might be presented as a graph, a color-coded representation, or some other easily interpretable format. The participant is instructed to modulate their brain activity based on the feedback they receive. This can involve various mental strategies, such as focusing attention on specific thoughts, images, or sensations. Over repeated sessions, participants learn to consciously influence their brain activity based on the feedback they receive. Through trial and error, they discover which mental strategies are most effective for achieving their desired changes in brain activity.
Neurofeedback learning
Visual fMRI neurofeedback (e.g., the diameter of a circle) will be provided to the participant at the end of each trial denoting the "level of engagement" in the instructed operation on that trial. The neurofeedback score is derived from the fMRI pattern classifier using regularized logistic regression for the instructed operation from a pre-trained working memory operation classifier. Scores range from 0 to 1, with higher scores indicating better fits to the target pattern of activity. The investigators will quantify neurofeedback learning by computing changes in feedback scores within each session and across the sessions.
Neural consequences of control
Proactive interference for the encoding of trial N, separately following maintain trials and suppress trials, will be assessed using within-subject item-specific activity pattern matching between the localizer data and the main task data. Proactive interference would be reflected in a reduced correspondence (lower scores) between the localizer representation for an item and the representation of that item when it is encoded on trial N. Higher scores would reflect a release from proactive interference.
Behavioral consequences of control
After each session, participants will be evaluated with behavioral memory tests on both positive and negative information. Two pictures (of the same valence) are presented for memorization on each trial, an instruction is given to manipulate the memory of one of those images, then a probe image appears and participants are to respond, as quickly and accurately as then can, YES if that probe image was one of the images presented at the beginning of the trial (even if the participant was asked to suppress that image) or NO otherwise. Half the probes contain new images. The accuracies and response times for this memory test will be statistically analyzed. The investigators will compare differences in these measures across condition (maintain vs. suppress) and cue type (manipulated vs. non-manipulated item). For the 3 neurofeedback sessions, the investigators will also relate a participant's neurofeedback scores with their behavioral performance on this memory test.
Training transfer across valence
The investigators will evaluate whether neurofeedback training transfers across emotional valence. Half of the participants will be trained on positive images, and half will be trained on negative images. The behavioral memory test at the end of each session includes both positive and negative images, and the outcomes measures from this test (as described in Primary Outcome 3) will be compared between these two groups of participants. Data will be statistically evaluated for whether the emotional valence on which a participant is trained has an effect that is selective (showing greater changes in accuracy and/or response times for trials of that same valence) or non-selective (no differences in performance metrics for trials with positive or negative valence).
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University of Texas at Austin
Lead sponsor
University of Colorado, Boulder
Collaborator
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