Examining the reorganization of cognitive networks in subjective cognitive decline: A network analysis approach
Bibliographic record
Abstract
Abstract Background Individuals with subjective cognitive decline (SCD) are at increased risk for developing dementia. However, research is still needed to identify what subtle cognitive changes in SCD might differentiate it from healthy aging. Network analysis has been used to model interrelationships between cognitive measures in healthy and clinically impaired older adults. These multivariate networks can capture a more holistic view of performance within and across cognitive domains, providing further insight into cognitive status. Studies of individuals with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) have revealed distinct network characteristics compared to cognitively normal (CN) older adults. We employed this novel technique to determine whether networks in SCD show evidence of reorganization compared to CN individuals. Method This project examined the cognitive network of a sample group with SCD (n = 207, 151 females/56 males, M Age = 71.6 ± 5.50, M Education = 15.5 ± 3.40) and compared it with networks of CN (n = 122, 94 females/28 males, M Age = 71.3 ± 6.08, M Education = 15.6 ± 3.41), MCI (n = 210, 100 females/110 males, M Age = 73.7 ± 6.55, M Education = 15.1 ± 3.81), and AD (n = 79, 32 females/47 males, M Age = 75.9 ± 7.33, M Education = 15.3 ± 4.34) participants. Group networks were comprised of performance on various cognitive tests from COMPASS‐ND1 and CIMA‐Q2 aging studies. Networks were constructed as Gaussian graphical models with EBICglasso regularization applied to return sparser networks3. Result Strength centrality indices (i.e., the absolute sum of partial correlations with other variables) suggest that measures of executive functioning become less influential while measures of long‐term memory increase their influence in SCD networks compared to CN networks, reflecting a shift from a common decrease in executive processing seen in normal aging to the episodic memory deficits characteristic of AD‐type cognitive decline. Conclusion These findings not only differentiate SCD networks from CN networks, but also suggest the beginnings of cognitive reorganization towards MCI, supporting the view of SCD as a preclinical state in the AD continuum. References: 1. Chertkow et al. (2019), https://doi.org/10.1017/cjn.2019.27 2. Belleville et al. (2019), https://doi.org/10.1016/j.dadm.2019.07.003 3. Epskamp & Fried (2018), https://doi.org/10.1037/met0000167
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".