Apathy and Attentional Biases in Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Apathy, one of the most prevalent neuropsychiatric symptoms in Alzheimer's disease (AD), can be difficult to assess as cognition deteriorates. There is a need for more objective assessments that do not rely on patient insight, communicative capacities, or caregiver observation. OBJECTIVE: We measured visual scanning behavior, using an eye-tracker, to explore attentional bias in the presence of competing stimuli to assess apathy in AD patients. METHODS: Mild-to-moderate AD patients (Standardized Mini-Mental Status Examination, sMMSE >10) were assessed for apathy (Neuropsychiatric Inventory [NPI] apathy, Apathy Evaluation Scale [AES]). Participants were presented with 16 slides, each containing 4 images of different emotional themes (2 neutral, 1 social, 1 dysphoric). The duration of time spent, and fixation frequency on images were measured. RESULTS: Of the 36 AD patients (14 females, age = 78.2±7.8, sMMSE = 22.4±3.5) included, 17 had significant apathy (based on NPI apathy ≥4) and 19 did not. These groups had comparable age and sMMSE. Repeated-measures analysis of covariance models, controlling for total NPI, showed group (apathetic versus non-apathetic) by image (social versus dysphoric) interactions for duration (F(1,32) = 4.31, p = 0.046) and fixation frequency (F(1,32) = 11.34, p = 0.002). Apathetic patients demonstrated reduced duration and fixation frequency on social images compared with non-apathetic patients. Additionally, linear regression models suggest that more severe apathy predicted decreasing fixation frequency on social images (R2 = 0.26, Adjusted R2 = 0.19, F(3,32) = 3.65, p = 0.023). CONCLUSION: These results suggest that diminished attentional bias toward social-themed stimuli is a marker of apathy in AD. Measurements of visual scanning behavior may have the potential to predict and monitor treatment response in apathy.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".