Latent patterns of task‐related functional connectivity relate to hyperactivation and associative memory impairment in SCD<sup>+</sup> and MCI
Bibliographic record
Abstract
Abstract Background Hyperactivation is commonly observed in prodromal Alzheimer’s disease (AD). It is however unknown if it relates to functional network dysfunction, and how hyperactivation‐networks relate to cognitive impairment. We used seed‐based connectivity to assess differences in correlations between activation of hyperactive regions, whole‐brain task‐related activation, and memory performance in persons with subjective cognitive decline plus (SCD+) and mild cognitive impairment (MCI). Method Data from 108 participants from the CIMA‐Q cohort were used in this study: 28 participants with SCD+ which presented with memory complaint and worry in addition to small hippocampal volume and/or were APOE4 carriers, 26 participants with MCI and 54 were healthy controls (HC). Task‐related activation was measured while participants memorized 78 pictures and their location in a four‐position grid. Multivariate seed‐partial least squares (seed‐PLS) analysis was used to identify latent variables (LVs) that assess between‐group differences and similarities in the three‐way association between 1) activation in hyperactive regions of interest (ROIs: left hippocampus, right inferior temporal gyrus (riTG), left superior parietal lobule (lsPL)), 2) whole‐brain activation associated with associative memory encoding, and 3) subsequent associative memory performance. Significance of the seed‐PLS effects were assessed with permutation testing and boostraps. Result Three LVs were significant (p < 0.05). LV1 (32.11 % of covariance explained) accounted for group similarities in the correlation between activity in all ROIs and activity in the fronto‐parietal and default mode networks. Interestingly, left hippocampus activity did not correlate in SCD+. LV2 (19.17 % of covariance explained) identified a network that was correlated with left hippocampal activity in all three groups; however, left hippocampal activity correlated with better memory in HC but with poorer memory in SCD+. LV3 (10.91 % of covariance explained) identified a network that was positively correlated to lsPL activity in SCD+, and correlated with seed activity in all ROIs and poorer memory performance in MCI. Conclusion Hyperactivation is associated with latent patterns of connectivity in prodromal AD, but different hyperactivation‐network associations relate to memory in SCD+ and MCI. Hyperactivation could serve as an early marker of network dysfunction in AD, and reflect the incoming cognitive symptomatology associated with the disease.
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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 | 0.001 | 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) | 0.002 | 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".