White Matter Integrity Is Associated With Intraindividual Variability in Neuropsychological Test Performance in Healthy Older Adults
Bibliographic record
Abstract
Inconsistency of performance across neuropsychological testing instruments (dispersion) shows sensitivity to acquired injury and neurodegenerative pathology in older adults. The underlying neural correlates have remained speculative however, in spite of known white matter degradation seen in conjunction with elevated inconsistency in related operationalizations of intraindividual variability. Consistently, these operationalizations have controlled for artifactual age-related variance to increase measurement sensitivity of CNS dysfunction. In this study, dispersion was examined alongside composite scores of memory and executive functioning from the Alzheimer’s Disease Neuroimaging Initiative. 44 healthy older adults (M=72.0, SD=6.4) underwent diffusion tensor imaging and neuropsychological testing spanning a range of cognitive domains. Results replicated previous findings, demonstrating reduced microstructural integrity with advanced age and increased integrity in high memory and executive functioning performers, across all major white matter tracts. With age first regressed from the composite scores, significant associations remained between greater executive functioning scores and greater microstructural integrity in the genu of corpus callosum, anterior corona radiata bilaterally, right internal capsule and right posterior thalamic radiation. With age regressed from the dispersion scores greater values were primarily associated with decreased white matter integrity in the body and genu of corpus callosum, anterior corona radiata bilaterally and left superior longitudinal fasciculus. Dispersion is easily computed across speeded and accuracy-based measures and shows promise in detecting white matter damage, beyond that seen in the typical aging process. This appears to be the first investigation of neural correlates associated with increased dispersion.
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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 | 0.003 |
| 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".