Posterior compensatory network in cognitively intact elders with hippocampal atrophy
Bibliographic record
Abstract
Functional compensation in late life is poorly understood but may be vital to understanding long-term cognitive trajectories. To study this we first established an empirically derived threshold to distinguish hippocampal atrophy in those with Mild Cognitive Impairment (MCI n = 34) from those with proficient cognition (PRO n = 22), using data from a population-based cohort. Next, to identify compensatory networks we compared cortical activity patterns during a graded spatial working memory (SWM) task in only cognitively proficient individuals, either with (PROATR ) or without hippocampal atrophy (PRONIL ). Multivariate Partial Least Squares analyses revealed that these groups engaged spatially distinct SWM-related networks. In those with hippocampal atrophy and under conditions of basic-SWM demand, expression of a posterior compensatory network (PCN) comprised calcarine and posterior parietal cortex strongly correlated with superior SWM performance (r = -0.96). In these individuals, basic level SWM response times were faster and no less accurate than in those with no hippocampal atrophy. Cognitively proficient older individuals with hippocampal atrophy may, therefore, uniquely engage posterior brain areas when performing simple spatial working memory tasks.
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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) | 0.001 | 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".