Network-based statistics reveals an enhanced subnetwork in prefrontal cortex in mild cognitive impairment: a functional near-infrared spectroscopy study
Bibliographic record
Abstract
Background Mild cognitive impairment (MCI) is generally considered to have a high risk of progression to Alzheimer’s disease. Our study aimed to investigate the abnormal functional connectivity (FC) in prefrontal cortex (PFC) in patients with MCI and explore the relationship between the observed changes and cognitive function. Methods Sixty-seven patients with MCI and 71 healthy individuals were recruited for this study. All participants underwent the Montreal Cognitive Assessment (MoCA) and functional near-infrared spectroscopy (fNIRS) examinations. Results Compared with healthy controls (HC), the patients with MCI exhibited significantly lower MoCA scores (p < 0.001). Through FC analysis, an enhanced subnetwork was observed in the right prefrontal cortex of the MCI group, covering four pairs of channel connections: CH12-CH15, CH12-CH16, CH13-CH15, and CH13-CH16. Moreover, the FC values of these four channel pairs and the education duration were significantly correlated with MoCA scores. Subsequently, a multiple linear regression model was performed to observe the independent factors of cognition decline, serving the education duration and the average FC values of subnetwork as independent variables and the MoCA scores as the dependent variable. The regression model showed a total of 25.7% explanation power (adjusted R2 = 0.257, F = 24.723, p < 0.001). Conclusion Our study suggested that the enhanced subnetwork within the right PFC may be involved in the pathophysiology of MCI and serve as a potential target for the treatment of MCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".