Reliable change in neuropsychological test scores is associated with brain atrophy in older adults
Bibliographic record
Abstract
The reliable change index (RCI) is a commonly used method for interpreting change in neuropsychological test scores over time. However, the RCI is a psychometric method that, to date, has not been validated against neuroanatomical changes. Longitudinal neuroimaging and neuropsychological data from baseline and one-year follow-up visits were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The RCI was used to identify participants showing reliable decline on memory (ADNI-Mem; N = 450) and executive functioning (ADNI-EF; N = 456) factor scores. For each factor score, two groups (reliable change vs. no reliable change) were matched on potential baseline confounding variables. Longitudinal neuroanatomical data were analysed using tensor-based morphometry. Analysis revealed that reliable change on ADNI-Mem was associated with atrophy in the medial temporal cortex, limbic cortex, temporal lobe and some regions of the parietal lobe. Similar atrophy patterns were found for reliable change on ADNI-EF, except that atrophy extended to the frontal lobe and the atrophy was more extensive and of higher magnitude. The current study not only validates clinical usage of the RCI with neuroanatomical evidence of associated underlying brain change but also suggests patterns of likely brain atrophy when reliable cognitive decline is detected.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".