Functional Magnetic Resonance Imaging Neurofeedback-guided Motor Imagery Training and Motor Training for Parkinson’s Disease: Randomized Trial
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Bibliographic record
Abstract
OBJECTIVE: Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) uses feedback of the patient's own brain activity to self-regulate brain networks which in turn could lead to a change in behavior and clinical symptoms. The objective was to determine the effect of NF and motor training (MOT) alone on motor and non-motor functions in Parkinson's Disease (PD) in a 10-week small Phase I randomized controlled trial. METHODS: Thirty patients with Parkinson's disease (PD; Hoehn and Yahr I-III) and no significant comorbidity took part in the trial with random allocation to two groups. Group 1 (NF: 15 patients) received rt-fMRI-NF with MOT. Group 2 (MOT: 15 patients) received MOT alone. The primary outcome measure was the Movement Disorder Society-Unified PD Rating Scale-Motor scale (MDS-UPDRS-MS), administered pre- and post-intervention "off-medication". The secondary outcome measures were the "on-medication" MDS-UPDRS, the PD Questionnaire-39, and quantitative motor assessments after 4 and 10 weeks. RESULTS: Patients in the NF group were able to upregulate activity in the supplementary motor area (SMA) by using motor imagery. They improved by an average of 4.5 points on the MDS-UPDRS-MS in the "off-medication" state (95% confidence interval: -2.5 to -6.6), whereas the MOT group improved only by 1.9 points (95% confidence interval +3.2 to -6.8). The improvement in the intervention group meets the minimal clinically important difference which is also on par with other non-invasive therapies such as repetitive Transcranial Magnetic Stimulation (rTMS). However, the improvement did not differ significantly between the groups. No adverse events were reported in either group. INTERPRETATION: This Phase I study suggests that NF combined with MOT is safe and improves motor symptoms immediately after treatment, but larger trials are needed to explore its superiority over active control conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it