Hippocampal Volume and the Detection of Mild Cognitive Impairment in an Older Adult Population: Assessing Performance on Cognitive Screeners Administered In-Person and Electronically
Bibliographic record
Abstract
The present study investigated how performance on in-person and electronic neuropsychological assessment measures predicted subcortical hippocampal volume and cognitive decline consistent with mild cognitive impairment. It was hypothesized that the Montreal Cognitive Assessment would display better predictive strength than the Cogstate Brief Battery when evaluating subcortical hippocampal volume measured via structural magnetic resonance imaging. It was further hypothesized that the Montreal Cognitive Assessment would be more sensitive to predicting group membership to the diagnostic classification of mild cognitive impairment compared to the Cogstate Brief Battery. The sample included 445 older adult participants selected from the Alzheimer’s Disease Neuroimaging Initiative 3. Participants met criteria for diagnostic classifications of cognitively normal and mild cognitive impairment and had undergone neuropsychological testing consisting of the Montreal Cognitive Assessment and Cogstate Brief Battery, as well as structural magnetic resonance imaging scans of the hippocampus at baseline testing. The learning/working memory composite from the Cogstate Brief Battery was the only substantial predictor for total subcortical hippocampal volume. When evaluating predictive strength relative to group membership of either cognitively normal or mild cognitive impairment, the Montreal Cognitive Assessment was the most substantial predictor of diagnostic classification, specifically mild cognitive impairment. The learning/working memory composite from the Cogstate Brief Battery was also a good predictor of group membership, though the Montreal Cognitive Assessment was observed to be more sensitive overall. The results of this study maintained the effectiveness of in-person neuropsychological assessment, while also supporting the use of electronic measures with older adults when evaluating cognitive status. The data also contributes additional information that is helpful in the early detection of progressive neurodegenerative diseases, such as Alzheimer’s disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".