Brain compensatory activation during Stroop task in patients with mild cognitive impairment: a functional near-infrared spectroscopy study
Bibliographic record
Abstract
Purpose This study investigated the disparities in brain activation patterns during the Stroop task among individuals with mild cognitive impairment (MCI) and those without any cognitive impairments (healthy controls, HCs) using functional near-infrared spectroscopy (fNIRS). Methods We analyzed the cortical activation patterns of 73 patients with MCI and 63 HC individuals as they completed the Stroop task, employing fNIRS. The regions of interest (ROIs) included the dorsal prefrontal cortex (dPFC), ventrolateral prefrontal cortex (VLPFC), and parietal lobe (PL). The Stroop task is divided into early stage (0–15 s) and late stage (15–30 s). We also measured participants’ behavior during the Stroop task, analyzed variations in cortical activation intensity at different experiment stages, and performed correlation analysis between Montreal Cognitive Assessment (MoCA) scores, Stroop performance, and oxygenation levels. Results Our analysis revealed that individuals with MCI and HC demonstrated elevated cortical activation in the dPFC, VLPFC, and PL areas while performing the Stroop task (q < 0.05, FDR-corrected). The MCI group displayed longer response latencies compared to the HC group while demonstrating comparable accuracy performance across both congruent and incongruent Stroop trials. The MCI group showed compensatory activation in the VLPFC, and PL regions compared to the HC group during the late stage of the Stroop task (q < 0.05, FDR-corrected). Correlational analysis revealed a negative association between MoCA scores and oxygenation levels in the dPFC, VLPFC, and PL regions during the late stage of the Stroop task (p < 0.05). However, no correlation was found with behavioral performance. Conclusion Mild cognitive impairment patients demonstrated effective compensation for their cognitive impairments at a partial behavioral level by engaging compensatory activation in the prefrontal, and parietal regions while performing the Stroop task.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".