Microstate-based Neurofeedback in Attention Deficit Hyperactivity Disorder Population: A Randomized Controlled Crossover Trial
Bibliographic record
Abstract
Neurofeedback (NFB) therapy based on spectral neuromarkers of Attention-Deficit/Hyperactivity Disorder (ADHD) has faced challenges regarding its efficacy and replicability. In this study, we investigate whether a microstate-based EEG marker, recently implicated in ADHD, could serve as a novel target for neurofeedback. Emerging research suggests that ADHD patients often exhibit an excess of microstate D, a state characterized by fronto-central cortical activity linked to attentional functions. This study aims to assess whether neurofeedback training can effectively modulate microstate D in adult ADHD patients, along with its short-term neurobehavioral correlates. We employed a within-subject, crossover design with 19 adults with ADHD, who participated in two counterbalanced neurofeedback sessions: one aimed at upregulating microstate D percent time coverage, and the other at downregulating it. While patients were able to volitionally increase microstate D during the upregulation session, no significant change was observed during the downregulation session. Direct comparison between the two sessions revealed that online control of microstate D was specific to the closed-loop feedback, rather than merely task engagement. No short-term effects of the neurofeedback sessions were observed. No moderate nor major adverse effects were reported. Despite lack of statistical power, this study provides controlled indicationfor the specificity and safety of neurofeedback training based on microstate D in adult ADHD patients. Although the short-term design did not yield clinical improvements, the findings demonstrate the feasibility of microstate-based neurofeedback protocols in a clinical population and offer valuable technical and methodological insights for designing futur studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".