Executive control network (ECN) and default mode network (DMN) functional connectivity in older adults with remitted depression, mild cognitive impairment, Alzheimer’s dementia, or normal cognition
Bibliographic record
Abstract
Abstract Background Major Depressive Disorder (MDD) is associated with an increased risk of developing Alzheimer’s dementia (AD). The present study aims to understand risk for AD by comparing resting state functional connectivity (FC) in putatively shared vulnerability networks; executive control network (ECN) or default mode network (DMN) among older adults who are either healthy or have a history of MDD, MCI, or AD. Method We assessed brain functional alterations in ECN and DMN in five groups at‐risk for dementia: remitted MDD, non‐amnestic mild cognitive impairment (naMCI), MDD+naMCI, amnestic MCI (aMCI), and MDD+aMCI. The analysis also included individuals with AD and healthy controls (HC). Resting‐state functional MRI data were acquired on the same 3T scanner. Following quality control (n=330), dual regression was conducted. The ECN and DMN group‐ICA (independent component analysis) were selected from the Smith atlas. Next, the group difference in the functional connectivity (FC) of each network was calculated using PALM (Permutation Analysis of Linear Models). Result For within‐network FC in the ECN or DMN, we found no difference between participant groups. For between‐network FC, we found a significant difference between the DMN and the superior parietal lobe (SPL); post‐hoc tests showed decreased anti‐correlation between the AD group compared to the MDD and HC groups (p<0.05). Conclusion The between‐network difference in DMN among groups was driven by the AD group. There was minimal distinction among those with remitted MDD or MCI. It is possible that remitted MDD may not act as mechanistic risk for AD in ECN or DMN networks.
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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) | 0.000 | 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".