The compensatory role of the frontal cortex in mild cognitive impairment: Identifying the target for neuromodulation
Bibliographic record
Abstract
Introduction: Development of individualized neuromodulation techniques for mild cognitive impairment (MCI) is a feasible practical goal. Preliminary research exploring the brain-level compensatory reserves on the base of neuroimaging is necessary. Methods: Twenty-one older adults, representing a continuum from healthy norm to MCI, underwent functional MRI while performing two executive tasks—a modified Stroop task and selective counting. A functional activation and connectivity analysis were conducted with the inclusion of a BRIEF–MoCA covariate. This variable represented the difference between the real-life performance measured by Behavior Rating Inventory of Executive Function (BRIEF) and the level of cognitive deficit measured by Montreal Cognitive Assessment (MoCA) Scale, an ability to compensate for impairment. Results: Both tasks were associated with activation of areas within the frontoparietal control network, along with the supplementary motor area (SMA) and the pre-SMA, the lateral premotor cortex, and the cerebellum. A widespread increase in the connectivity of the pre-SMA was observed during the tasks. The BRIEF–MoCA value correlated, first, with connectivity of the left dorsolateral prefrontal cortex (LDLPFC) and, second, with enrollment of the occipital cortex during the counting task. Conclusion: The developed neuroimaging technique allows identification of the functionally salient target within the LDLPFC in patients with MCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".