Multimodal neural correlates of cognitive awareness in aging and Alzheimer’s disease
Bibliographic record
Abstract
Impaired cognitive awareness-anosognosia-is a core symptom of Alzheimer's disease (AD), yet its neural correlates remain poorly defined. This study examined how cognitive awareness, measured both cross-sectionally and longitudinally via subject-informant discrepancy on the Everyday Cognition (ECog) questionnaire, relates to three AD biomarkers: amyloid burden, glucose hypometabolism, and cortical atrophy. We included 785 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), spanning cognitively normal (CN), mild cognitive impairment (MCI), and AD dementia. All biomarkers were assessed at baseline across the same 86 cortical regions, enabling anatomically harmonized, cross-modality comparisons. Linear mixed models incorporating all three biomarkers revealed no significant associations in CN. In MCI, declining awareness was associated with widespread cortical amyloid deposition (significant in 80/86 regions), sparing some limbic areas. Atrophy in 11 regions-including limbic, lateral temporal, and occipital cortices-also predicted awareness decline (all p < 0.044). In AD, no significant associations were found between amyloid and awareness, suggesting a plateau effect at advanced stages. In both MCI and AD, lower baseline glucose metabolism in the left posterior cingulate cortex (PCC) was associated with poorer awareness (MCI: β ± SE = - 0.14 ± 0.04, p = 0.006; AD: β ± SE = - 0.24 ± 0.07, p = 0.042). No significant biomarker-time interactions were found in AD, suggesting relatively stable awareness levels at advanced disease stages. These findings indicate that anosognosia in AD is linked to distinct biomarker and regional profiles that vary by disease phase. Multimodal analysis across harmonized regions reveals the left PCC as a robust metabolic correlate of awareness, underscoring its potential as a key target in understanding and monitoring self-awareness impairment in neurodegenerative 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".