An fNIRS-based investigation of cerebral hemodynamic responses during verbal fluency task and n-back task in individuals with mild cognitive impairment
Bibliographic record
Abstract
Background: Early detection of mild cognitive impairment (MCI) is crucial for preventing Alzheimer's disease (AD). This study aims to explore alterations in brain co-functional connectivity between cognitively healthy individuals and those with cognitive impairment during a verbal fluency task (VFT) using functional near-infrared spectroscopy (fNIRS). The investigation examines changes in brain activation patterns in both MCI patients and healthy controls during the VFT and 1-back task, and identifies correlations between cognitive function and brain activation areas using fNIRS technology. Methods: This study evaluated markers for screening MCI by performing the VFT and 1-back task using a 67-channel fNIRS to measure changes in oxyhemoglobin (HbO) levels in the bilateral prefrontal and temporal lobes of 108 healthy controls (HC) and 103 participants with MCI. The severity of patients' symptoms was assessed using the Montreal Cognitive Assessment (MoCA) scale, neuropsychiatric symptoms were evaluated with the Symptom Checklist-90 (SCL-90), and sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Results: Compared with the HC group, the MCI group showed a significant reduction in MoCA scores, with no significant differences in education level, PSQI, and SCL-90 scores. There was no significant difference in brain activation levels between the MCI and HC groups during the VFT. However, during the 1-back task, the MCI group exhibited significantly reduced activation levels in channels 33, 54, 49, and 47, as well as in the dorsolateral prefrontal cortex (DLPFC) and frontal eye fields (FEF). Moreover, the mean HbO levels in these channels, DLPFC, and FEF during the 1-back task were found to be significantly correlated with MoCA scores. Discussion: When performing the VFT and 1-back task, our study found that patients with MCI exhibited reduced brain activity levels in the DLPFC and FEF only during the 1-back task. This diminished task-induced brain activity was significantly positively correlated with MoCA scores and was less influenced by mental health and sleep quality. The 1-back task may be a more optimal paradigm for the early detection of MCI compared to the VFT.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".