Functional and clinical outcomes of FMRI-based neurofeedback training in patients with alcohol dependence: a pilot study
Bibliographic record
Abstract
Identifying treatment options for patients with alcohol dependence is challenging. This study investigates the application of real-time functional MRI (rtfMRI) neurofeedback (NF) to foster resistance towards craving-related neural activation in alcohol dependence. We report a double-blind, placebo-controlled rtfMRI study with three NF sessions using alcohol-associated cues as an add-on therapy to the standard treatment. Fifty-two patients (45 male; 7 female) diagnosed with alcohol dependence were recruited in Munich, Germany. RtfMRI data were acquired in three sessions and clinical abstinence was evaluated 3 months after the last NF session. Before the NF training, BOLD responses and clinical data did not differ between groups, apart from anger and impulsiveness. During NF training, BOLD responses of the active group were decreased in medial frontal areas/caudate nucleus, and increased, e.g. in the cuneus/precuneus and occipital cortex. Within the active group, the down-regulation of neuronal responses was more pronounced in patients who remained abstinent for at least 3 months after the intervention compared to patients with a relapse. As BOLD responses were comparable between groups before the NF training, functional variations during NF cannot be attributed to preexisting distinctions. We could not demonstrate that rtfMRI as an add-on treatment in patients with alcohol dependence leads to clinically superior abstinence for the active NF group after 3 months. However, the study provides evidence for a targeted modulation of addiction-associated brain responses in alcohol dependence using rtfMRI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".