<p>Associations of informant-based sleep reports with Alzheimer’s disease pathologies</p>
Bibliographic record
Abstract
Purpose: Recent studies have found associations of increased brain amyloid beta (Aβ) accumulation and several abnormal sleep-wake patterns, including shorter latency and increased fragmentation in preclinical Alzheimer’s disease (AD). There is little known about the relationship between sleep and tau. The objective of this study was to understand the associations of both tau and Aβ with early signs of sleep and night-time behavior changes in clinically normal elderly adults. Specifically, we have addressed the question of how informant-based subjective sleep reports are linked to regional [ 18 F]flortaucipir and [ 18 F]florbetapir uptake. Methods: Imaging and behavioral data from 35 subjects were obtained from the Alzheimer’s Disease Neuroimaging Initiative. The Neuropsychiatric Inventory Sleep (NPI-sleep) Questionnaire was used to assess the sleep and night-time behavior changes. Regional tau-positron emission tomography (PET) (entorhinal, brainstem) and Aβ-PET (posterior cingulate, precuneus, medial orbitofrontal) uptake values were calculated. A series of linear regression analyses were used to determine the combination of sleep symptoms that built the best models to predict each pathology. Results: Informant-based reports of abnormal night-time behavior (NPI questions k3, k5, and k8) were significantly associated with increased entorhinal tau and Aβ (all regions) accumulation. Interestingly, informant-based reports of sleep deficiencies without abnormal nigh-time activity (NPI questions k1, k2, and k6) were negatively associated with entorhinal tau burden. Conclusion: Detection of abnormal night-time behaviors (wandering, pacing, other inappropriate activities) by family members indicates early signs of both AD pathologies and may encourage the affected individuals to seek help by health care providers for detailed cognitive/neurobehavioral assessments. Keywords: Aβ, tau, positron emission tomography, mild cognitive impairment, sleep, neuropsychiatric inventory
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".