SMR/Theta Neurofeedback Training Improves Cognitive Performance and EEG Activity in Elderly With Mild Cognitive Impairment: A Pilot Study
Bibliographic record
Abstract
Background. Neurofeedback (NF) training, as a method of self-regulation of brain activity, may be beneficial in elderly patients with mild cognitive impairment. In this pilot study, we investigated whether a sensorimotor (SMR)/ theta NF training could improve cognitive performance and brain electrical activity in elderly patients with mild cognitive impairment. Methods. Twenty elderly patients with mild cognitive impairment (MCI) were assigned to 20 consecutive sessions of sensorimotor (SMR)/ theta NF training, during 10 weeks, on a basis of 2 sessions each week. Neuropsychological assessments and questionnaires as well as electroencephalogram (EEG) were performed and compared between baseline (T0), after the last NF training session at 10 weeks (T1), and one-month follow-up (T2). Results. Repeated measures ANOVA reveal that from baseline to post-intervention, participants showed significant improvement in the Montreal cognitive assessment (MoCa, F= 4.78; p=0.012), the delayed recall of the Rey auditory verbal learning test (RAVLT, F= 3.675; p=0.032), the Forward digit span (F= 13.82; p<0.0001), the Anxiety Goldberg Scale (F=4.54; p=0.015), the Wechsler Adult Intelligence Score –Fourth Edition (WAIS-IV) (F=24.75; p<0.0001), and the Mac Nair score (F=4.47 ; p=0.016). EEG theta power (F=4.44; p= 0.016) and alpha power (F=3.84; p=0.027) during eyes-closed resting state significantly increased after the NF training, and showed sustained improvement at one-month follow-up. Conclusion. Our results suggest that NF training could be effective to reduce cognitive deficits in elderly patients with mild cognitive impairment, and improve their EEG activity. If these findings are confirmed by randomized controlled studies with larger samples of patients, NF could be seen as useful non-invasive, non-pharmacological tool for preventing further decline, rehabilitation of cognitive function in elderly. Trial Registration: This pilot study was a preliminary step before the trial registered in ClinicalTrials.gov under the number of NCT03526692.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".